Dissertations / Theses on the topic 'EEG, electroencephalogram'
To see the other types of publications on this topic, follow the link: EEG, electroencephalogram.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'EEG, electroencephalogram.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
2
Duta, Mihaela D. "The study of vigilance using neural networks analysis of EEG." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301454.
3
Lee, Pamela Wen-Hsin. "Mutual information derived functional connectivity of the electroencephalogram (EEG)." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/219.
Abstract:
Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.
4
Mathew, Blesy Anu. "ENTROPY OF ELECTROENCEPHALOGRAM (EEG) SIGNALS CHANGES WITH SLEEP STATE." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/203.
Abstract:
We hypothesized that temporal features of EEG are altered in sleep apnea subjects comparedto normal subjects. The initial aim was to develop a measure to discriminate sleep stages innormals. The longer-term goal was to apply these methods to identify differences in EEGactivity in sleep apnea subjects from normals. We analyzed the C3A2 EEG and anelectrooculogram (EOG) recorded from 9 normal adults awake and in rapid eye movement(REM) and non-REM sleep. The EEG signals were filtered to remove EOG contamination. Twomeasures of the irregularity of EEG signals, Sample Entropy (SpEn) and Tsallis Entropy, wereevaluated for their ability to discriminate sleep stages. SpEn changes with sleep state, beinglargest in Wake. Stage 3/4 had the smallest SpEn (0.57??0.11) normalized to Wake values,followed by Stage 2 (0.72??0.09), REM (0.75??0.1) and Stage 1 (0.89??0.05). This pattern wasconsistent in all the polysomnogram records analyzed. Similar pattern was observed in leadO1A2 as well. We conclude that SpEn may be useful as part of a montage for assessing sleepstate. We analyzed data from sleep apnea subjects having obstructive and central apnea eventsand have made some preliminary observations; the SpEn values were more similar across sleepstages and also high correlation with oxygen saturation was observed.
5
Riddington, Edward Peter. "Automated interpretation of the background EEG using fuzzy logic." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1109.
Abstract:
A new framework is described for managing uncertainty and for dealing with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective expertise of the experienced electroencephalbgrapher, and the lack of a reliable means of dealing with the range of EEG artefacts (signal contamination). In this thesis, a new framework is described which introduces objectivity in two important outcomes of clinical evaluation of the EEG, namely, the clinical factual report and the clinical 'conclusion', by capturing the subjective expertise of the electroencephalographer and dealing with the problem of artefact corruption. The framework is separated into two stages .to assist piecewise optimisation and to cater for different requirements. The first stage, 'quantitative analysis', relies on novel digital signal processing algorithms and cluster analysis techniques to reduce data and identify and describe background activities in the EEG. To deal with artefact corruption, an artefact removal strategy, based on new reUable techniques for artefact identification is used to ensure that artefact-free activities only are used in the analysis. The outcome is a quantitative analysis, which efficiently describes the background activity in the record, and can support future clinical investigations in neurophysiology. In clinical practice, many of the EEG features are described by the clinicians in natural language terms, such as very high, extremely irregular, somewhat abnormal etc. The second stage of the framework, 'qualitative analysis', captures the subjectivity and linguistic uncertainty expressed.by the clinical experts, using novel, intelligent models, based on fuzzy logic, to provide an analysis closely comparable to the clinical interpretation made in practice. The outcome of this stage is an EEG report with qualitative descriptions to complement the quantitative analysis. The system was evaluated using EEG records from 1 patient with Alzheimer's disease and 2 age-matched normal controls for the factual report, and 3 patients with Alzheimer's disease and 7 age-matched nonnal controls for the 'conclusion'. Good agreement was found between factual reports produced by the system and factual reports produced by qualified clinicians. Further, the 'conclusion' produced by the system achieved 100% discrimination between the two subject groups. After a thorough evaluation, the system should significantly aid the process of EEG interpretation and diagnosis.
6
D'ROZARIO, Angela Louise. "Electroencephalogram (EEG) biomarkers of neurobehavioural dysfunction in obstructive sleep apnea." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/9886.
Abstract:
Obstructive sleep apnea (OSA) affects an estimated 2-4% of middle–aged adults yet we are still exploring how best to delineate the neurophysiological deficits that accompany this disorder. Untreated OSA leads to an increased risk of motor vehicle accidents. Traditional polysomnographic (PSG) metrics do not consistently correlate with daytime functioning. There is a clinical need for simple biomarkers to identify individuals susceptible to OSA-related cognitive deficits. There is a close relationship between EEG-based changes in brain activity and daytime function in healthy sleepers. No studies have explored quantitative EEG (qEEG) biomarkers during baseline sleep and resting wakefulness (baseline) as correlates of waking neurobehavioural performance during extended wakefulness in OSA. The aims of the thesis were 1) to identify qEEG biomarkers of neurobehavioural dysfunction and sleepiness in OSA and controls during 40-hours (h) of extended wakefulness, and 2) to develop and validate automated EEG artefact processing methods for subsequent qEEG analysis of waking and sleep EEG. EEG biomarkers were derived using conventional power spectral analysis and a novel qEEG analysis technique, detrended fluctuation analysis (DFA). This study showed that wake qEEG markers significantly correlated with impaired performance and increased sleepiness across 40-h of extended wakefulness in both groups. Baseline waking measures of the DFA scaling exponent, but not power spectra, were associated with impaired simulated driving after 24-h awake in OSA. Furthermore, OSA patients with greater EEG slowing during REM sleep showed a marked decline in performance after 24-h awake. These key findings highlight the potential utility of qEEG analysis to yield simple biomarkers of neurobehavioural impairment and sleepiness. Automated EEG artefact processing methods for resting awake and PSG recordings were developed and validated against a reference-standard of manual artefact recognition as part of this study. These proven artefact processing methods will be pivotal for exploring qEEG biomarkers in future studies.
7
Löfhede, Johan. "Classification of Burst and Suppression in the Neonatal EEG." Licentiate thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3448.
Abstract:
The brain requires a continuous supply of oxygen and even a short period ofreduced oxygen supply risks severe and lifelong consequences for theaffected individual. The delivery is a vulnerable period for a baby who mayexperience for example hypoxia (lack of oxygen) that can damage the brain.Babies who experience problems are placed in an intensive care unit wheretheir vital signs are monitored, but there is no reliable way to monitor thebrain directly. Monitoring the brain would provide valuable informationabout the processes going on in it and could influence the treatment and helpto improve the quality of neonatal care. The scope of this project is todevelop methods that eventually can be put together to form a monitoringsystem for the brain that can function as decision-support for the physician incharge of treating the patient.The specific technical problem that is the topic of this thesis is detection ofburst and suppression in the electroencephalogram (EEG) signal. The thesisstarts with a brief description of the brain, with a focus on where the EEGoriginates, what types of activity can be found in this signal and what theymean. The data that have been available for the project are described,followed by the signal processing methods that have been used for preprocessing,and the feature functions that can be used for extracting certaintypes of characteristics from the data are defined. The next section describesclassification methodology and how it can be used for making decisionsbased on combinations of several features extracted from a signal. Theclassification methods Fisher’s Linear Discriminant, Neural Networks andSupport Vector Machines are described and are finally compared with respectto their ability to discriminate between burst and suppression. An experimentwith different combinations of features in the classification has also beencarried out. The results show similar results for the three methods but it canbe seen that the SVM is the best method with respect to handling multiplefeatures.
8
Murrell, Joanna. "Spontaneous EEG changes in the equine surgical patient." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340352.
9
Tcheslavski, Gleb V. "Coherence and Phase Synchrony Analysis of Electroencephalogram." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/30186.
Abstract:
Phase Synchrony (PS) and coherence analyses of stochastic time series - tools to discover brain tissue pathways traveled by electrical signals - are considered for the specific purpose of processing of the electroencephalogram (EEG).
We propose the Phase Synchrony Processor (PSP), as a tool for implementing phase synchrony analysis, and examine its properties on the basis of known signals. Long observation times and wide filter bandwidths can decrease bias in PS estimates. The value of PS is affected by the difference in frequency of the sequences being analyzed and can be related to that frequency difference by the periodic sinc function.
PS analysis of the EEG shows that the average PS is higher - for a number of electrode pairs - for non-ADHD than for ADHD participants. The difference is more pronounced in the δ rhythm (0-3 Hz) and in the γ rhythm (30-50 Hz) PS. The Euclidean classifier with electrode masking yields 66 % correct classification on average for ADHD and non-ADHD subjects using the δ and γ1 rhythms.
We observed that the average γ1 rhythm PS is higher for the eyes closed condition than for the eyes open condition. The latter may potentially be used for vigilance monitoring. The Euclidean discriminator with electrode masking shows an average percentage of correct classification of 78 % between the eyes open and eyes closed subject conditions.
We develop a model for a pair of EEG electrodes and a model-based MS coherence estimator aimed at processing short (i.e. 20 samples) EEG frames. We verify that EEG sequences can be modeled as AR(3) processes degraded by additive white noise with an average SNR of approximately 11-12 dB.
Application of the MS coherence estimator to the EEG suggests that MS coherence is generally higher for non-ADHD individuals than for ADHD participants when evaluated for the θ rhythm of EEG. Also, MS coherence is consistently higher for ADHD subjects than for the majority of non-ADHD individuals when computed for the low end of the δ rhythm (i.e. below 1 Hz).
ADHD produces more measurable effects in the frontal lobe EEG and for participants performing attention intensive tasks.Ph. D.
10
Ascolani, Gianluca. "EEG, Alpha Waves and Coherence." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc28389/.
Abstract:
This thesis addresses some theoretical issues generated by the results of recent analysis of EEG time series proving the brain dynamics are driven by abrupt changes making them depart from the ordinary Poisson condition. These changes are renewal, unpredictable and non-ergodic. We refer to them as crucial events. How is it possible that this form of randomness be compatible with the generation of waves, for instance alpha waves, whose observation seems to suggest the opposite view the brain is characterized by surprisingly extended coherence? To shed light into this apparently irretrievable contradiction we propose a model based on a generalized form of Langevin equation under the influence of a periodic stimulus. We assume that there exist two different forms of time, a subjective form compatible with Poisson statistical physical and an objective form that is accessible to experimental observation. The transition from the former to the latter form is determined by the brain dynamics interpreted as emerging from the cooperative interaction among many units that, in the absence of cooperation would generate Poisson fluctuations. We call natural time the brain internal time and we make the assumption that in the natural time representation the time evolution of the EEG variable y(t) is determined by a Langevin equation perturbed by a periodic process that in this time representation is hardly distinguishable from an erratic process. We show that the representation of this random process in the experimental time scale is characterized by a surprisingly extended coherence. We show that this model generates a sequence of damped oscillations with a time behavior that is remarkably similar to that derived from the analysis of real EEG's. The main result of this research work is that the existence of crucial events is not incompatible with the alpha wave coherence. In addition to this important result, we find another result that may help our group, or any other research group working on the analysis of brain's dynamics, to prove or to disprove the existence of crucial events. We study the diffusion process generated by fluctuations emerging from the same model after filtering out the alpha coherence, and we study the recursion to the origin. We study the survival probability of this process, namely the probability that up to a given time no re-crossing of the origin occurs. We find that this is an inverse power law with a power that depends on whether or not crucial events exist.
11
Holt, Mark Rowan Gorton. "The use of neural networks in the analysis of the anaesthetic electroencephalogram." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390525.
12
Roessgen, Mark Andrew. "Analysis and modelling of EEG data with application to seizure detection in the newborn." Thesis, Queensland University of Technology, 1996. https://eprints.qut.edu.au/105543/1/T%28BE%26E%29%201033%20Analysis%20and%20modelling%20of%20EEG%20data%20with%20application%20to%20seizure%20detection%20in%20the%20newborn.pdf.
Abstract:
The exact relationship between the electroencephalogram (EEG) measured at the scalp, and the internal dynamical organisation of the brain that generated it, is still very much an open question. This has led to difficulties
in EEG analysis and interpretation, which in turn has relegated the EEG largely to the role of a corroborative, rather than stand alone diagnostic tool. This thesis investigates the use of signal processing techniques for more accurate quantification of the EEG. It is anticipated that through improved quantification, a better diagnostic value for the EEG will result.
13
Rodriguez, Ricardo J. "An Electroencephalogram (EEG) Based Biometrics Investigation for Authentication| A Human-Computer Interaction (HCI) Approach." Thesis, Nova Southeastern University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3723216.
Abstract:
Encephalogram (EEG) devices are one of the active research areas in human-computer interaction (HCI). They provide a unique brain-machine interface (BMI) for interacting with a growing number of applications. EEG devices interface with computational systems, including traditional desktop computers and more recently mobile devices. These computational systems can be targeted by malicious users. There is clearly an opportunity to leverage EEG capabilities for increasing the efficiency of access control mechanisms, which are the first line of defense in any computational system.
Access control mechanisms rely on a number of authenticators, including “what you know”, “what you have”, and “what you are”. The “what you are” authenticator, formally known as a biometrics authenticator, is increasingly gaining acceptance. It uses an individual’s unique features such as fingerprints and facial images to properly authenticate users. An emerging approach in physiological biometrics is cognitive biometrics, which measures brain’s response to stimuli. These stimuli can be measured by a number of devices, including EEG systems.
This work shows an approach to authenticate users interacting with their computational devices through the use of EEG devices. The results demonstrate the feasibility of using a unique hard-to-forge trait as an absolute biometrics authenticator by exploiting the signals generated by different areas of the brain when exposed to visual stimuli. The outcome of this research highlights the importance of the prefrontal cortex and temporal lobes to capture unique responses to images that trigger emotional responses.
Additionally, the utilization of logarithmic band power processing combined with LDA as the machine learning algorithm provides higher accuracy when compared against common spatial patterns or windowed means processing in combination with GMM and SVM machine learning algorithms. These results continue to validate the value of logarithmic band power processing and LDA when applied to oscillatory processes.
14
Babaeeghazvini, Parinaz. "EEG enhancement for EEG source localization in brain-machine speller." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6016.
Abstract:
A Brain-Computer Interface (BCI) is a system to communicate with external world through the brain activity. The brain activity is measured by Electro-Encephalography (EEG) and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEGbased brain–computer interface (BCI). In this thesis BCI methods were applied on derived sources which by their EEG enhancement it became possible to obtain a more accurate EEG detection and brought a new application to BCI technology that are recognition of writing letters imagery from brain waves. The BCI system enables people to write and type letters by their brain activity (EEG). To this end, first part of the thesis is dedicated to EEG source reconstruction techniques to select the most optimal EEG channels for task classification purposes. Due to this reason the changes in EEG signal power from rest state to motor imagery task was used, to find the location of an active single equivalent dipole. Implementing an inverse problem solution on the power changes by Multiple Sparse Priors (MSP) method generated a scalp map where its fitting showed the localization of EEG electrodes. Having the optimized locations the secondary objective was to choose the most optimal EEG features and rhythm for an efficient classification. This became possible by feature ranking, 1- Nearest Neighbor leave-one-out. The feature vectors were computed by applying the combined methods of multitaper method, Pwelch. The features were classified by several methods of Normal densities based quadratic classifier (qdc), k-nearest neighbor classifier (knn), Mixture of Gaussians classification and Train neural network classifier using back-propagation. Results show that the selected features and classifiers are able to recognize the imagination of writing alphabet with the high accuracy.BCI controls external devices and interacts with the environment by brain signals. Measured EEG signals over the motor cortex exhibit changes in power related to the movements or imaginations which are executed in motor tasks [1]. These changes declare increase or decrease of power in the alpha (8Hz-13Hz), and beta (13Hz-28Hz) frequency bands from resting state to motor imagery task that known as event related synchronization (in case of power increasing) and desynchronization (in case of power decreasing) [2]. The necessity to communicate with the external world for locked-in state (LIS) patients (a paralyzed patient who only communicates with eyes), made doctors and engineers motivated to develop a BCI technology for typing letters through brain commands. Many researches have been done around this area to ascertain the dream of typing for handicapped. In the brain some regions of the cerebral cortex (motor cortex) are involved in the planning, control, and execution of voluntary movements. Electroencephalography (EEG) signals are electrical potential generated by the nerve cells in the cerebral cortex. In order to execute motoric tasks, the EEG signals are appeared over the motor cortex [1]. The measured brain response to a stimulus is called eventrelated potential (ERP). P300-event related potential (ERP) is an evoked neuron response to an external auditory or visual stimulus that is detectable in scalp-recorded EEG (The P300 is evoked potential which occurs across the parieto-central on the skull 300 ms after applying the stimulus). Farwell and Donchin have proven in a P300-based BCI speller [3] that P300 response is a reliable signal for controlling a BCI system. They described the P300 speller, in which alphanumeric characters are represented in a matrix grid of six-by-six matrix. The user should focus on one of the 36 character cells while each row and column of the grid is intensified randomly and sequentially. The P300, observed in EEG signals, is created by the intersection of the target row and column which causes detection of the target stimuli with a probability of 1/6 (in case of high accuracy of flashing operation). Also when the target stimulus is rarely presented in the random sequence of stimuli causes a neural reaction to unpredictable but recognizable event and a P300 response is evoked [3]. Generally when the subject is involved with the task to recognize the targets, the P300 wave happens and the signal amplitude varies with the unlikelihood of the targets. Its dormancy changes with the difficulty of recognizing the target stimulus from the standard stimuli [3].The attended character of the matrix can be extracted by proper feature extraction and classification of P300. A plenty of procedures for feature extraction and classification have been applied to improve the performance of originally reported speller [3], such as stepwise linear discriminate analysis (SWLDA) [4, 5], wavelets [1], support vector machines [6, 7, 8] and matched filtering [9]. Till now, BCI-related P300 research has mostly considered on signals from standard P300 scalp locations. While in [10, 11, 12, 13, 14, 15, 16] it has been proven that the use of additional locations, especially posterior sites, may improve classification accuracy, but it has not been addressed to particular offline and online studies. Recently, auditory version improvement of the visual P300 speller allows locked in patients who have problem in the visual system to use the P300 speller system by relating two numbers to each letter which indicate the row and column of letter position [17]. Now a new technology is needed which can substitute a keyboard with no alphabet menu. The technology will be handy for blind people and useful for healthy persons who need to work hands free with their computer or mobile. The aim of this thesis is to improve EEG detection through source localization for a new BCI application to type with EEG signals without using alphabet menu.
+98-9359576229
15
Zamora, Mayela E. "The study of the sleep and vigilance electroencephalogram using neural network methods." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365699.
16
Wu, Shuai. "Mot robust cross-subject klassificering av electroencephalogram (EEG) baserad brain-computer interfacing (BCI):En genomförbarhetsstudie." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254765.
Abstract:
Brain-computer interface(BCI) är ett system där man kan skicka kommandon till dator med bara hjärnaktivitet. En sådan system är viktigt för människor lider av flera motorisk funktionshinder, då maskinen skulle kunna förbättra patienters liv genom att uppfylla deras behov. Denna rapport fokusera på en variant av BCI, kallas motor imagery based BCI, vilken basera på att klassificera försökspersons hjärnaktivitet då han/hon tänka sig att röra sin kroppsdelar. Det finns flera svårighet för att bygga en fungerande system, en av de är generalisering av tränad model. En tränad model garanti inte exakthet på annat försöksperson eller annat session. Även i samma session, kan model ger sämre resultat på grund av hjärnaktiviteten nonstationary natur. Denna rapport försöka hantera inter-subject klassificering problem med adaptive importance weighted linear discriminant analysis(AIWLDA), som gav bra resultat i både intra-session och inter-session klassificering av offline EEG baserad BCI. Det kommer visa i resultat att det finns försökspersons par där inter-subject generalisering är möjligt och AIWLDA kan avslöja mer av sådana par, men misslyckas att bevisa om det denna egenskap finns mellan alla försöksperson.A brain-computer interface (BCI) is a system that enables the subject to send commands with merely brain activity. Such interface is important for people affected by multiple motor disabilities, where BCI made it possible for machine to better understand the patient and thus fulfill their demands. The BCI variante that base on motor imagery require classification on subject’s brain activity on imagining movement of body parts, which could be done by using different classifier. There exists multiple difficulty when developing such an system, one of them is generalization of trained models, this accuracy of trained model could not be guaranteed when using on a different subject or in a different session. Even within the same session, the classification result is not optimal due to brain activity’s non-stationary nature. This paper tackle the problem of intersubject classification with adaptive importance weighted linear discriminant analysis(AIWLDA), which shows promising result on both intersession and intra-session classification of offline EEG based BCI. This research has shown that there exist subject pairs with inter-subject generalizable potential, more pairs could be revealed by using AIWLDA, but this method fail to robustly classify across every subject-pairs.
17
Qassim, Yahya Taher. "FPGA Design and Implementation of Wavelet Coherence for EEG Signals." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/366086.
Abstract:
The EEG waveform provides millisecond resolution brain information that can be obtained from the scalp using metal electrodes. It has become an applicable measure for a wide range of brain functionalities (including higher cognition) due to its low cost, non-invasiveness and ease of access. An important EEG application uses an evoked form of these signals linked to an external stimulus. For this thesis, an EEG was acquired during presentation of an oddball task and recording the event related potential (ERP), in which the P300 component is the most important. It reflects the participant’s response to rare or occasional stimulus events. Extracting features from these non-stationary signals can be achieved with a time-frequency method such as the continuous wavelet transform (CWT) whereas examining the functional connectivity between a pair of brain channels, as a source of EEG, can be achieved with the wavelet coherence (WC). However, the real time processing of these two digital signal processing (DSP) algorithms, which imply a large number of computations, requires running them with minimal delay for use in real time biofeedback applications.
To achieve the required speed of processing for real time EEG applications, the involvement of hardware computation is required. One of the well-known hardware platforms in the field of DSP is the Field Programmable Gate Array (FPGA). These devices allow digital implementation of a wide range of DSP algorithms with a high processing speed, in addition to their configurability and portability. The aim of this thesis was the FPGA design and implementation of WC for EEG signals.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Engineering
Science, Environment, Engineering and Technology
Full Text
18
Koppikar, Samir Dilip. "Privacy Preserving EEG-based Authentication Using Perceptual Hashing." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955127/.
Abstract:
The use of electroencephalogram (EEG), an electrophysiological monitoring method for recording the brain activity, for authentication has attracted the interest of researchers for over a decade. In addition to exhibiting qualities of biometric-based authentication, they are revocable, impossible to mimic, and resistant to coercion attacks. However, EEG signals carry a wealth of information about an individual and can reveal private information about the user. This brings significant privacy issues to EEG-based authentication systems as they have access to raw EEG signals.
This thesis proposes a privacy-preserving EEG-based authentication system that preserves the privacy of the user by not revealing the raw EEG signals while allowing the system to authenticate the user accurately. In that, perceptual hashing is utilized and instead of raw EEG signals, their perceptually hashed values are used in the authentication process. In addition to describing the authentication process, algorithms to compute the perceptual hash are developed based on two feature extraction techniques. Experimental results show that an authentication system using perceptual hashing can achieve performance comparable to a system that has access to raw EEG signals if enough EEG channels are used in the process. This thesis also presents a security analysis to show that perceptual hashing can prevent information leakage.
19
Vennelaganti, Swetha. "AGING AND SLEEP STAGE EFFECTS ON ENTROPY OF ELECTROENCEPHALOGRAM SIGNALS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/553.
Abstract:
The aging brain is characterized by alteration in synaptic contacts, which leads to decline of motor and cognitive functions. These changes are reflected in the age related shifts in power spectrum of electroencephalogram (EEG) signals in both wakefulness and sleep. Various non-linear measures have been used to obtain more insights from EEG analysis compared to the conventional spectral analysis. In our study we used Sample Entropy to quantify regularity of the EEG signal. Because elderly subjects arouse from sleep more often than younger subjects, we hypothesized that Entropy of EEG signals from elderly subjects would be higher than that from middle aged subjects, within a sleep stage. We also hypothesized that the entropy increases during and following an arousal and does not return to background levels immediately after an arousal. Our results show that Sample Entropy varies systematically with sleep state in healthy middle-aged and elderly female subjects, reflecting the changing regularity in the EEG. Sample Entropy is significantly higher in elderly in sleep Stage 2 and REM, suggesting that in these two sleep stages the cortical state is closer to wake than in middle-aged women. Sample Entropy is higher in post-arousal compared to the pre-arousal and stays high for a 30 sec period.
20
Alhajjar, Yasser. "Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG." Thesis, Angers, 2017. http://www.theses.fr/2017ANGE0020/document.
Abstract:
L’électroencéphalogramme (EEG), mesure de l'activité électrique du cerveau, reste une des meilleures méthodes de prévision non-invasive des résultats neurologiques. L'objectif de notre travail est de développer un système de classification automatique qui prévoit des risques sur la maturation cérébrale, se traduisant par un état pathologique à 2 ans. Les caractéristiques du signal EEG, qui sont utiles à la prévision automatisée, sont traitées via un module appelée EEGDiag, et sont appliquées sur un ensemble de données issues de 397 dossiers de nouveau-nés prématurés. Chaque dossier comprend un enregistrement EEG, et un rapport concernant les informations et les diagnostics de l’enfant à la naissance et 2 ans plus tard (normal, pathologique ou douteux). Pour aider les médecins à prévenir l’état neurologique anormal du nouveau-né prématuré, nous avons développé plusieurs modèles de classification qui s’appliquent sur différentes séries de caractéristiques du signal EEG inspirées des annotations des neuropédiatres. Plusieurs modèles de classification et d’aide à la décision sont testés sur différentes extractions de la base de données afin de fournir aux médecins le système de classification le plus performant. Notre système proposé permet de détecter automatiquement des pronostics sur l’état pathologique du nouveau-né prématuré. Notre travail a consisté à subdiviser l’amplitude des bouffées du signal EEG en trois catégories : faible, moyenne et forte. Cette étude de subdivision a permis de choisir les intervalles associés à ces trois catégories permettant d’augmenter considérablement la performance de notre système de classification automatique. Une analyse de corrélation a permis de détecter des relations d’indépendance et de redondance entre certaines données, ce qui permet de réduire le nombre de variables décisives et de sélectionner ainsi la meilleure série de variables qui ramènent notre système à devenir optimal et plus efficace. Ces études nous ont permis d’atteindre un système de classification automatique basé sur une série de 17 variables avec une exactitude 93.2%. Ce système peut apporter une bonne sensibilité à la prévision de l’état neurologique du nouveau-né prématuré et peut servir comme aide à la décision dans le traitement cliniqueThe electroencephalogram (EEG), a measure of the electrical activity of the brain, remains one of the best non-invasive methods for predicting neurological outcomes. The aim of our work is to develop an automatic classification system which predicts risks on cerebral maturation that can lead to a pathological condition at 2 years. The EEG signal characteristics, which are useful for automated prediction, are processed via an application called EEGDiag, and applied to a set of 397 records for premature infants. Each record include an EEG record and a report on infant information and diagnosis at birth and 2 years later (normal, sick or risky). To assist physicians in preventing any abnormal neurological condition of the premature newborn, we have developed several intelligent classification models which can be applied to several series of characteristics of the EEG inspired from the annotations of neuropediatricians. Several classification and decisional aid models have been tested on different extracted databases in order to offer to doctors the best efficient classification system. Our proposed system automatically detects the prognosis of the premature newborn pathological condition. Our work consisted of subdividing the amplitude of EEG signal burst into three categories: low, medium and high. This subdivision study allowed to choose Intervals of these three categories which have served to greatly increase the performance of our intelligent classification system. A correlative data analysis allowed to create an independence and redundancy relation between the data attributes, which reduces the number of decisive parameters and thus selects the best series of parameters that made our system optimal and more efficient. These studies enabled us to achieve a classification system based on a series of 17 parameters with an accuracy 93.2%. This system can provide good sensitivity on predicting the neurological status of premature newborn and can be used as a decisional aid in clinical treatment
21
Crossen, Samantha Lokelani. "Investigation of Variability in Cognitive State Assessment based on Electroencephalogram-derived Features." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316025164.
22
Sadovský, Petr. "Analýza spánkového EEG." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2007. http://www.nusl.cz/ntk/nusl-233411.
Abstract:
This thesis deals with analysis and processing of the Sleep Electroencephalogram (EEG) signals. The scope of this thesis can be split into several areas. The first area is application of the Independent Component Analysis (ICA) method for EEG signal analysis. A model of EEG signal formation is proposed and conditions under which this model is valid are examined. It is shown that ICA can be used to remove non-deterministic artifacts contained in the EEG signals. The second area of interest is analysis of stationarity of the Sleep EEG signal. Methods to identify stationary signal segments and to analyze statistical properties of these stationary segments are presented. The third area of interest focuses on spectral analysis of the Sleep EEG signals. Analyses are performed that shows the processes that form particular parts of EEG signals spectrum. Also, random signals that are an integral part of the EEG signals analysis are performed. The last area of interest focuses on elimination of the transition processes that are caused by the filtering of the short EEG signal segments.
23
Hassanpour, Hamid. "Time-frequency based detection of newborn EEG seizure." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15853/1/Hamid_Hassanpour_Thesis.pdf.
Abstract:
Neurological diseases in newborns are usually first revealed by seizures, which are characterised by a synchronous discharge of a large number of neurons. Failure to control seizures may lead to brain damage or even death.
The importance of this problem prompted many researchers to look for accurate automatic methods for seizure detection. Nonstationarity and multicomponent behaviour of newborn EEG signals made this task very challenging. The significant overlap in the characteristic of background and seizure activities in newborn EEG signals added to the difficulty of seizure detection.
This research uses time-frequency based methods for automatic seizure detection. Since time-frequency signal analysis methods use joint representation in both time and frequency domains, they proved to be very suitable for analysis and processing of nonstationary and multicomponent signals such as newborn EEG.
Before using any seizure detector, the EEG data is pre-processed in order to reduce the noise effects using a time-frequency based technique. The proposed method is based on the singular value decomposition (SVD) technique applied to the matrix representing the time-frequency distribution (TFD) of the EEG signal. It has been shown that by appropriately filtering the singular vectors associated with the TFD, one can effectively enhance the desired information embedded in the signal.
Neonatal EEG seizures can have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. The seizure detection techniques proposed in the literature concentrated on using either low frequency or high frequency signatures but not both simultaneously. These methods tend to miss the seizures that reveal themselves only in one of the two frequency areas. In this research, we propose a detection method that uses seizure features in both low and high frequency areas.
To detect EEG seizures using the low frequency signatures, an SVD-based technique is employed. The technique uses the estimated distribution function of the singular vectors associated with the time-frequency distribution of EEG epochs to discriminate between seizure and nonseizure patterns.
The high frequency signatures of seizures are mostly the result of spike events in the EEG signals. To detect these spike events, the signal is mapped into the TF domain. The high instantaneous energy of spikes is reflected as a localised energy in the high frequency area of the TF domain. Consequently, a spike can be seen as a ridge in this area of the TF domain. It has been shown that during seizure activity there is regularity in the distribution of the interspike intervals. This feature has been used as the basis for discriminating between seizure and nonseizure patterns.
The performance results obtained by applying the proposed methods on EEG signals extracted from a number of newborns show the superiority of these methods over the existing ones.
24
Hassanpour, Hamid. "Time-Frequency Based Detection of Newborn EEG Seizure." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15853/.
Abstract:
Neurological diseases in newborns are usually first revealed by seizures, which are characterised by a synchronous discharge of a large number of neurons. Failure to control seizures may lead to brain damage or even death. The importance of this problem prompted many researchers to look for accurate automatic methods for seizure detection. Nonstationarity and multicomponent behaviour of newborn EEG signals made this task very challenging. The significant overlap in the characteristic of background and seizure activities in newborn EEG signals added to the difficulty of seizure detection. This research uses time-frequency based methods for automatic seizure detection. Since time-frequency signal analysis methods use joint representation in both time and frequency domains, they proved to be very suitable for analysis and processing of nonstationary and multicomponent signals such as newborn EEG. Before using any seizure detector, the EEG data is pre-processed in order to reduce the noise effects using a time-frequency based technique. The proposed method is based on the singular value decomposition (SVD) technique applied to the matrix representing the time-frequency distribution (TFD) of the EEG signal. It has been shown that by appropriately filtering the singular vectors associated with the TFD, one can effectively enhance the desired information embedded in the signal. Neonatal EEG seizures can have signatures in both low frequency (lower than 10 Hz) and high frequency (higher than 70 Hz) areas. The seizure detection techniques proposed in the literature concentrated on using either low frequency or high frequency signatures but not both simultaneously. These methods tend to miss the seizures that reveal themselves only in one of the two frequency areas. In this research, we propose a detection method that uses seizure features in both low and high frequency areas. To detect EEG seizures using the low frequency signatures, an SVD-based technique is employed. The technique uses the estimated distribution function of the singular vectors associated with the time-frequency distribution of EEG epochs to discriminate between seizure and nonseizure patterns. The high frequency signatures of seizures are mostly the result of spike events in the EEG signals. To detect these spike events, the signal is mapped into the TF domain. The high instantaneous energy of spikes is reflected as a localised energy in the high frequency area of the TF domain. Consequently, a spike can be seen as a ridge in this area of the TF domain. It has been shown that during seizure activity there is regularity in the distribution of the interspike intervals. This feature has been used as the basis for discriminating between seizure and nonseizure patterns. The performance results obtained by applying the proposed methods on EEG signals extracted from a number of newborns show the superiority of these methods over the existing ones.
25
Nussbaum, Paul. "Signal Processing of Electroencephalogram for the Detection of Attentiveness towards Short Training Videos." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/558.
Abstract:
This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of attentiveness across many participants, pinpointing areas in the training videos that induce peak attentiveness. Qualitative analysis of the results of this research is also very promising. The system produces attentiveness graphs for individual participants and these triangulate well with the thoughts and feelings those participants had during different parts of the videos, as described in their own words. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. This research was motivated by this interest, as well as recent advances in electronic and computer engineering’s use of biometric signal analysis for the detection of affective (emotional) response. Signal processing of EEG has proven useful in measuring alertness, emotional state, and even towards very specific applications such as whether or not participants will recall television commercials days after they have seen them. This research extended these advances by creating an automated system which measures attentiveness towards short training videos. The bulk of the research was focused on electrical and computer engineering, specifically the optimization of signal processing algorithms for this particular application. A review of existing methods of EEG signal processing and feature extraction methods shows that there is a common subdivision of the steps that are used in different EEG applications. These steps include hardware sensing filtering and digitizing, noise removal, chopping the continuous EEG data into windows for processing, normalization, transformation to extract frequency or scale information, treatment of phase or shift information, and additional post-transformation noise reduction techniques. A large degree of variation exists in most of these steps within the currently documented state of the art. This research connected these varied methods into a single holistic model that allows for comparison and selection of optimal algorithms for this application. The research described herein provided for such a structured and orderly comparison of individual signal analysis and feature extraction methods. This study created a concise algorithmic approach in examining all the aforementioned steps. In doing so, the study provided the framework for a systematic approach which followed a rigorous participant cross validation so that options could be tested, compared and optimized. Novel signal analysis methods were also developed, using new techniques to choose parameters, which greatly improved performance. The research also utilizes machine learning to automatically categorize extracted features into measures of attentiveness. The research improved existing machine learning with novel methods, including a method of using per-participant baselines with kNN machine learning. This provided an optimal solution to extend current EEG signal analysis methods that were used in other applications, and refined them for use in the measurement of attentiveness towards short training videos. These algorithms are proven to be best via selection of optimal signal analysis and optimal machine learning steps identified through both n-fold and participant cross validation. The creation of this new system which uses signal processing of EEG for the detection of attentiveness towards short training videos has created a significant advance in the field of attentiveness measuring towards short training videos.
26
Montgomery, Mason. "OPTIMIZATION OF FEATURE SELECTION IN A BRAIN-COMPUTER INTERFACE SWITCH BASED ON EVENT-RELATED DESYNCHRONIZATION AND SYNCHRONIZATION DETECTED BY EEG." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2786.
Abstract:
There are hundreds of thousands of people who could benefit from a Brain-Computer Interface. However, not all are willing to undergo surgery, so an EEG is the prime candidate for use as a BCI. The features of Event-Related Desynchronization and Synchronization could be used for a switch and have been in the past. A new method of feature selection was proposed to optimize classification of active motor movement vs a non-active idle state. The previous method had pre-selected which frequency and electrode to use as electrode C3 at the 20Hz bin. The new method used SPSS statistical software to determine the most significant frequency and electrode combination. This improved method found increased accuracy in classifying cases as either active or idle states. Future directions could be using multiple features for classification and BCI control, or exploiting the difference between ERD and ERS, though for either of these a more advanced algorithm would be required.
27
Segalotto, Matheus. "ARNI: an EEG-Based Model to Measure Program Comprehension." Universidade do Vale do Rio dos Sinos, 2018. http://www.repositorio.jesuita.org.br/handle/UNISINOS/7019.
Abstract:
Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-24T13:44:05Z
No. of bitstreams: 1
Matheus Segalotto_.pdf: 8717126 bytes, checksum: 94fda4721d448e49b82be91aaa8057c7 (MD5)Made available in DSpace on 2018-04-24T13:44:05Z (GMT). No. of bitstreams: 1 Matheus Segalotto_.pdf: 8717126 bytes, checksum: 94fda4721d448e49b82be91aaa8057c7 (MD5) Previous issue date: 2018-01-18
CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
PROSUP - Programa de Suporte à Pós-Gradução de Instituições de Ensino Particulares
A compreensão de programa é um processo cognitivo realizado no cérebro dos desenvolvedores para entender o código-fonte. Este processo cognitivo pode ser influenciado por vários fatores, incluindo o nível de modularização do código-fonte e o nível de experiência dos desenvolvedores de software. A compreensão de programa é amplamente reconhecida como uma tarefa com problemas de erro e esforço. No entanto, pouco foi feito para medir o esforço cognitivo dos desenvolvedores para compreender o programa. Além disso, esses fatores influentes não são explorados no nível de esforço cognitivo na perspectiva dos desenvolvedores de software. Além disso, alguns modelos de cognição foram criados para detectar indicadores de atividade cerebral, bem como dispositivos de eletroencefalografia (EEG) para suportar essas detecções. Infelizmente, eles não são capazes de medir o esforço cognitivo. Este trabalho, portanto, propõe o ARNI, um modelo computacional baseado em EEG para medir a compreensão do programa. O modelo ARNI foi produzido com base em lacunas encontradas na literatura após um estudo de mapeamento sistemático (SMS), que analisou 1706 estudos, 12 dos quais foram escolhidos como estudos primários. Um experimento controlado com 35 desenvolvedores de software foi realizado para avaliar o modelo ARNI através de 350 cenários de compreensão de programa. Além disso, esse experimento também avaliou os efeitos da modularização e a experiência dos desenvolvedores no esforço cognitivo dos desenvolvedores. Os resultados obtidos sugerem que o modelo ARNI foi útil para medir o esforço cognitivo. O experimento controlado revelou que a compreensão do código fonte não modular exigia menos esforço temporal (34,11%) e produziu uma taxa de compreensão mais alta (33,65%) do que o código fonte modular. As principais contribuições são: (1) a execução de SMS no contexto estudado; (2) um modelo computacional para medir a compreensão do programa para medir o código-fonte; (3) conhecimento empírico sobre os efeitos da modularização no esforço cognitivo dos desenvolvedores. Finalmente, este trabalho pode ser visto como um primeiro passo para uma agenda ambiciosa na área de compreensão de programa.
Program comprehension is a cognitive process performed in the developers’ brain to understand source code. This cognitive process may be influenced by several factors, including the modularization level of source code and the experience level of software developers. The program comprehension is widely recognized as an error-prone and effort-consuming task. However, little has been done to measure developers’ cognitive effort to comprehend program. In addition, such influential factors are not explored at the cognitive effort level from the perspective of software developers. Additionally, some cognition models have been created to detect brain-activity indicators as well as wearable Electroencephalography (EEG) devices to support these detections. Unfortunately, they are not able to measure the cognitive effort. This work, therefore, proposes the ARNI, an EEG-Based computational model to measure program comprehension. The ARNI model was produced based on gaps found in the literature after a systematic mapping study (SMS), which reviewed 1706 studies, 12 of which were chosen as primary studies. A controlled experiment with 35 software developers was performed to evaluate the ARNI model through 350 scenarios of program comprehension. Moreover, this experiment also evaluated the effects of modularization and developers’ experience on the developers’ cognitive effort. The obtained results suggest that the ARNI model was useful to measure cognitive effort. The controlled experiment revealed that the comprehension of non-modular source code required less temporal effort (34.11%) and produced a higher correct comprehension rate (33.65%) than modular source code. The main contributions are: (1) the execution of SMS in the context studied; (2) a computational model to measure program comprehension to measure source code; (3) empirical knowledge about the effects of modularization on the developers’ cognitive effort. Finally, this work can be seen as a first step for an ambitious agenda in the area of program comprehension.
28
Estepp, Justin Ronald. "An improved adaptive filtering approach for removing artifact from the electroencephalogram." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1433244703.
29
HERATH, MUTHUKUMARA MUDIYANSELAGE Samantha Chandani. "Using EEG measures to quantify reduced daytime vigilance in patients diagnosed with obstructive sleep apnoea using a novel electroencephalogram analysis method." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9726.
Abstract:
Introduction Vigilance in obstructive sleep apnoea (OSA) does not correlate well with disease severity/ symptoms: Hence the need for a simple objective test. One such method could be quantitative analysis of the awake electroencephalogram (qEEG). qEEG is conventionally analysed using Power Spectral Analysis (PSA) looking at different EEG frequencies of delta, theta, alpha and beta. A novel method of analyzing the qEEG: De-trended fluctuation analysis (DFA) provides a single value: the scaling exponent (SE), which measures the fluctuations in the EEG signal. Artefact removal from qEEG is mandatory with the gold standard being manual scoring. Another method of automated artefact removal is independent component analysis (ICA). Objective Investigate the role of PSA and DFA (SE) as an objective measure of testing vigilance and validate the use of ICA in patients diagnosed with OSA. Methodology Retrospective cross-sectional study of untreated OSA patients. Results ICA and manual artefact removal gave well-correlated results in the DFA (SE), but not PSA. EEG slowing measured by PSA and DFA did not correlate to impaired performance during a battery of 14 separate performance tests. Conclusion ICA and manual artefact removal can be interchangeably used in extracting DFA measurements with confidence. In PSA metrics the use of ICA may not be reliable.
30
Mahadevan, Anandi. "Real Time Ballistocardiogram Artifact Removal in EEG-fMRI Using Dilated Discrete Hermite Transform." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1226235813.
31
Loughran, Sarah Patricia, and n/a. "The efffects of eletromagnetic fields emitted by mobile phones on human sleep and melatonin production." Swinburne University of Technology, 2007. http://adt.lib.swin.edu.au./public/adt-VSWT20070731.100218.
Abstract:
The use of mobile phones is continually increasing throughout the world, with
recent figures showing that there are currently more than 2 billion mobile
phone users worldwide. However, despite the recognised benefits of the
introduction and widespread use of mobile phone technologies, concerns
regarding the potential health effects of exposure to the radiofrequency
electromagnetic fields emitted by mobile phone handsets have similarly
increased, leading to an increase in demand for scientific research to
investigate the possibility of health effects related to the use of mobile
phones.
An increasing amount of radiofrequency bioeffects research related to mobile
phone use has focussed on the possible effects of mobile phone exposure on
human brain activity and function, particularly as the absorption of energy in
the head and brain region is much higher than in other body regions, which is
a direct result from the close proximity of the mobile phone to the head when
in normal use. In particular, the use of sleep research has become a more
widely used technique for assessing the possible effects of mobile phones on
human health and wellbeing, and is particularly useful for providing important
information in the establishment of possible radiofrequency bioeffects,
especially in the investigation of potential changes in sleep architecture
resulting from mobile phone use.
A review of the previous literature showed that a number of studies have
reported an increase in the electroencephalogram spectral power within the 8
� 14 Hz frequency range in both awake and sleep states following
radiofrequency electromagnetic field exposure. In regards to sleep, the
enhancements reported have not been entirely consistent, with some early
studies failing to find an effect, while more recent studies have reported that
the effect differs in terms of particular frequency range. However, in general
the previous literature suggests that there is an effect of mobile phone
emissions on the sleep electroencephalogram, particularly in the frequency
range of sleep spindle activity.
In addition to changes in spectral power, changes in other conventional sleep
parameters and the production and secretion of melatonin have also been
investigated, however, there has been little or no consistency in the findings
of previous studies, with the majority of recent studies concluding that there
is no influence of mobile phone radiofrequency fields on these parameters of
sleep or melatonin.
Following a detailed review of the previous research, the current study was
developed with the aim to improve on previous methodological and statistical
limitations, whilst also being the largest study to investigate mobile phone
radiofrequency bioeffects on human sleep. The principle aims were thus to
test for the immediate effects of mobile phone radiofrequency
electromagnetic fields on human sleep architecture and the secretion of the
pineal hormone, melatonin.
The experiment included 50 participants who were randomly exposed to
active and sham mobile phone exposure conditions (one week apart) for 30
minutes prior to a full night-time sleep episode. The experimental nights
employed a randomised exposure schedule using a double-blind crossover
design. Standard polysomnography was used to measure subsequent sleep,
and in addition, participants were required to provide urine samples
immediately following exposure and upon waking in the morning. A full
dosimetric assessment of the exposure system was also performed in order
to provide sufficient details of the exposure set-up used in the current thesis
and to account for the lack of detailed dosimetric data provided in the
majority of previous studies.
The results of the current study suggest that acute exposure to a mobile
phone prior to sleep significantly enhances electroencephalogram spectral
power in the sleep spindle frequency range compared to the sham exposure
condition. The current results also suggest that this mobile phone-induced
enhancement in spectral power is largely transitory and does not linger
throughout the night. Furthermore, a reduction in rapid eye movement sleep
latency following mobile phone exposure was also found compared to the
sham exposure, although interestingly, neither this change in rapid eye
movement sleep latency or the enhancement in spectral power following
mobile phone exposure, led to changes in the overall quality of sleep.
Finally, the results regarding melatonin suggested that, overall, overnight
melatonin secretion is unaffected by acute exposure to a mobile phone prior
to sleep.
In conclusion, the current study has confirmed that a short exposure to the
radiofrequency electromagnetic fields emitted by a mobile phone handset
immediately prior to sleep is sufficient to induce changes in brain activity in
the initial part of sleep. The consequences or functional significance of this
effect are currently unknown and it would be premature to draw conclusions
about possible health consequences based on the findings of the current
study.
32
Zarjam, Pega. "EEG Data acquisition and automatic seizure detection using wavelet transforms in the newborn EEG." Queensland University of Technology, 2003. http://eprints.qut.edu.au/15795/.
Abstract:
This thesis deals with the problem of newborn seizre detection from the Electroencephalogram (EEG) signals. The ultimate goal is to design an automated seizure detection system to assist the medical personnel in timely seizure detection. Seizure detection is vital as neurological diseases or dysfunctions in newborn infants are often first manifested by seizure and prolonged seizures can result in impaired neuro-development or even fatality. The EEG has proved superior to clinical examination of newborns in early detection and prognostication of brain dysfunctions. However, long-term newborn EEG signals acquisition is considerably more difficult than that of adults and children. This is because, the number of the electrodes attached to the skin is limited by the size of the head, the newborns EEGs vary from day to day, and the newborns are reluctant of being in the recording situation. Also, the movement of the newborn can create artifact in the recording and as a result strongly affect the electrical seizure recognition. Most of the existing methods for neonates are either time or frequency based, and, therefore, do not consider the non-stationarity nature of the EEG signal. Thus, notwithstanding the plethora of existing methods, this thesis applies the discrete wavelet transform (DWT) to account for the non-stationarity of the EEG signals. First, two methods for seizure detection in neonates are proposed. The detection schemes are based on observing the changing behaviour of a number of statistical quantities of the wavelet coefficients (WC) of the EEG signal at different scales. In the first method, the variance and mean of the WC are considered as a feature set to dassify the EEG data into seizure and non-seizure. The test results give an average seizure detection rate (SDR) of 97.4%. In the second method, the number of zero-crossings, and the average distance between adjacent extrema of the WC of certain scales are extracted to form a feature set. The test obtains an average SDR of 95.2%. The proposed feature sets are both simple to implement, have high detection rate and low false alarm rate. Then, in order to reduce the complexity of the proposed schemes, two optimising methods are used to reduce the number of selected features. First, the mutual information feature selection (MIFS) algorithm is applied to select the optimum feature subset. The results show that an optimal subset of 9 features, provides SDR of 94%. Compared to that of the full feature set, it is clear that the optimal feature set can significantly reduce the system complexity. The drawback of the MIFS algorithm is that it ignores the interaction between features. To overcome this drawback, an alternative algorithm, the mutual information evaluation function (MIEF) is then used. The MIEF evaluates a set of candidate features extracted from the WC to select an informative feature subset. This function is based on the measurement of the information gain and takes into consideration the interaction between features. The performance of the proposed features is evaluated and compared to that of the features obtained using the MIFS algorithm. The MIEF algorithm selected the optimal 10 features resulting an average SDR of 96.3%. It is also shown, an average SDR of 93.5% can be obtained with only 4 features when the MIEF algorithm is used. In comparison with results of the first two methods, it is shown that the optimal feature subsets improve the system performance and significantly reduce the system complexity for implementation purpose.
33
Zarjam, Peggy. "EEG Data acquisition and automatic seizure detection using wavelet transforms in the newborn EEG." Thesis, Queensland University of Technology, 2003. https://eprints.qut.edu.au/15795/1/Pega_Zarjam_Thesis.pdf.
Abstract:
This thesis deals with the problem of newborn seizre detection from the Electroencephalogram (EEG) signals. The ultimate goal is to design an automated seizure detection system to assist the medical personnel in timely seizure detection. Seizure detection is vital as neurological diseases or dysfunctions in newborn infants are often first manifested by seizure and prolonged seizures can result in impaired neuro-development or even fatality.
The EEG has proved superior to clinical examination of newborns in early detection and prognostication of brain dysfunctions. However, long-term newborn EEG signals acquisition is considerably more difficult than that of adults and children. This is because, the number of the electrodes attached to the skin is limited by the size of the head, the newborns EEGs vary from day to day, and the newborns are reluctant of being in the recording situation. Also, the movement of the newborn can create artifact in the recording and as a result strongly affect the electrical seizure recognition.
Most of the existing methods for neonates are either time or frequency based, and, therefore, do not consider the non-stationarity nature of the EEG signal. Thus, notwithstanding the plethora of existing methods, this thesis applies the discrete wavelet transform (DWT) to account for the non-stationarity of the EEG signals. First, two methods for seizure detection in neonates are proposed. The detection schemes are based on observing the changing behaviour of a number of statistical quantities of the wavelet coefficients (WC) of the EEG signal at different scales. In the first method, the variance and mean of the WC are considered as a feature set to dassify the EEG data into seizure and non-seizure. The test results give an average seizure detection rate (SDR) of 97.4%. In the second method, the number of zero-crossings, and the average distance between adjacent extrema of the WC of certain scales are extracted to form a feature set. The test obtains an average SDR of 95.2%. The proposed feature sets are both simple to implement, have high detection rate and low false alarm rate.
Then, in order to reduce the complexity of the proposed schemes, two optimising methods are used to reduce the number of selected features. First, the mutual information feature selection (MIFS) algorithm is applied to select the optimum feature subset. The results show that an optimal subset of 9 features, provides SDR of 94%. Compared to that of the full feature set, it is clear that the optimal feature set can significantly reduce the system complexity. The drawback of the MIFS algorithm is that it ignores the interaction between features.
To overcome this drawback, an alternative algorithm, the mutual information evaluation function (MIEF) is then used. The MIEF evaluates a set of candidate features extracted from the WC to select an informative feature subset. This function is based on the measurement of the information gain and takes into consideration the interaction between features. The performance of the proposed features is evaluated and compared to that of the features obtained using the MIFS algorithm. The MIEF algorithm selected the optimal 10 features resulting an average SDR of 96.3%. It is also shown, an average SDR of 93.5% can be obtained with only 4 features when the MIEF algorithm is used.
In comparison with results of the first two methods, it is shown that the optimal feature subsets improve the system performance and significantly reduce the system complexity for implementation purpose.
34
CHIKUSHI, Rohgi Toshio Meneses. "Análise não linear de sinais de EEG : uma aplicação de redes complexas." Universidade Federal Rural de Pernambuco, 2014. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6720.
Abstract:
Submitted by (edna.saturno@ufrpe.br) on 2017-03-30T14:56:43Z
No. of bitstreams: 1
RohgiToshio Meneses Chikushi.pdf: 6493487 bytes, checksum: b95c0c692d050783c78c20f7a212f0e6 (MD5)Made available in DSpace on 2017-03-30T14:56:43Z (GMT). No. of bitstreams: 1 RohgiToshio Meneses Chikushi.pdf: 6493487 bytes, checksum: b95c0c692d050783c78c20f7a212f0e6 (MD5) Previous issue date: 2014-08-29
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
The electroencephalogram (EEG) is still an important tool in the diagnosis of neurodiseases. As recording technique offers an excellent temporal resolution, instantly capturing brain electrical activity. Recent studies suggest that non-linear dynamic time series as EEG can be transformed into complex networks by the methods of visibility graph and the recurrence network. The builded complex network allows many parameters or network metrics to characterize normal and epleptics. In this work, we transform EEG signals to complex networks and identify the metrics to find statistical diferences between normal and epleptical groups. We show that exist significant statistical differences in the network metrics from the normals and epileptics conditions. We conclude that the transformation of the EEG signal in complex networks provide a helpful tool to diagnostic the brain states.
O eletroencefalograma (EEG) ainda é uma ferramenta importante no diagnóstico de desordens neurológicas. Como técnica de registro, oferece uma excelente resolução temporal, capturando instantaneamente a atividade cerebral. Estudos recentes em dinâmica não linear sugerem que séries temporais como o EEG podem ser transformadas em redes complexas por meio de mapeamentos como o método de visibilidade e o de recorrência. Essas redes, em analogia às rede neuronais, representam as características de complexidade dinâmica do sistema nervoso. Neste trabalho, transformamos sinais de EEG em redes complexas derivadas da reconstrução dos espaços de fase, com base no conceito de recorrência. A aplicação de redes complexas na análise não linear da dinâmica da atividade cerebral, possibilitou diferenciar estados normais e epilépticos por meio da comparação das medidas topológicas dessas redes. Identificamos diferenças significativas ao compararmos os registros de EEG em condições normais e epilépticas usando as métricas das redes e concluímos que a transformação do EEG em redes complexas fornece um grande número de parâmetros úteis para caracterização e possível diagnóstico dos estados do comportamento cerebral normal e epiléptico.
35
Labounek, René. "Analýza souvislostí mezi simultánně měřenými EEG a fMRI daty." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219743.
Abstract:
Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.
36
Price, Gregory Walter. "Application of time series analysis techniques to the human electroencephalogram in real time, in order to synchronise event related potentials (ERPS) with background EEG." Thesis, Queensland University of Technology, 1995.
37
Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16200/1/Luke_Rankine_Thesis.pdf.
Abstract:
Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing.
The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG.
Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis.
A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG.
Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes.
The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit.
An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%).
In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.
38
Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16200/.
Abstract:
Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.
39
Janeček, David. "Sdružená EEG-fMRI analýza na základě heuristického modelu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221334.
Abstract:
The master thesis deals with the joint EEG-fMRI analysis based on a heuristic model that describes the relationship between changes in blood flow in active brain areas and in the electrical activity of neurons. This work also discusses various methods of extracting of useful information from the EEG and their influence on the final result of joined analysis. There were tested averaging methods of electrodes interest, decomposition by principal components analysis and decomposition by independent component analysis. Methods of averaging and decomposition by PCA give similar results, but information about a stimulus vector can not be extracted. Using ICA decomposition, we are able to obtain information relating to the certain stimulation, but there is the problem in the final interpretation and selection of the right components in a blind search for variability coupled with the experiment. It was found out that although components calculated from the time sequence EEG are independent for each to other, their spectrum shifts are correlated. This spectral dependence was eliminated by PCA / ICA decomposition from vectors of spectrum shifts. For this method, each component brings new information about brain activity. The results of the heuristic approach were compared with the results of the joined analysis based on the relative and absolute power approach from frequency bands of interest. And the similarity between activation maps was founded, especially for the heuristic model and the relative power from the gamma band (20-40Hz).
40
Rasheed, S. "RECOGNITION OF PRIMARY COLOURS IN ELECTROENCEPHALOGRAPH SIGNALS USING SUPPORT VECTOR MACHINES." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155486.
Abstract:
In this study we have worked on the classification of EEG signals produced by the exposure of primary colours (RGB). The main goal of this study was to perform an offline analysis and classification of color information obtained from EEG signals recorded in response to individual RGB colours presentation in order to verify our hypothesis, if the observation of different colors can be detected or not by selecting different frequency bands. We have also performed an offline analysis of EEG signals produced by the colour imagination to observe similarities in EEG signals between actual color exposure and their corresponding imagination in order to find a Way-In to further establish our argument for developing future BCI applications that utilizes colour information from EEG signals unlike the Wadsworth and Graz noninvasive BCI applications that utilizes sensory motor rhythm. It was seen that it is possible to detect the information, not only of actual colour exposure but also the information of colours imagination, from EEG signals. It was also seen that the colour information obtained through the imagination of colours was similar to the actual colour exposure in some subjects. The experiment was designed in a way to expose the colours to the subjects in random order of presentation and also their corresponding imaginations. Different features are extracted and analyzed. The EEG signals have to be classified into Red, Green and Blue classes.
We have used Support Vector Machines with event-related spectral perturbation as features for the classification task using three different kernels, linear, polynomial and RBF which came out with the average classification accuracy of 84% with linear, 89% with polynomial and 97% with RBF kernel for real exposure of colors whereas for imagination of colors accuracy was 64%, 70% and 76% respectively. As an alternative, we have also performed extreme energy ratio (EER) and extreme energy difference (EED) criterions to extract energy features using only linear kernel with SVM. The classification was performed on three different groups of colors i.e. (Blue, Green), (Red, Green) and (Red, Blue). The accuracies found with both of EER and EED are (79%, 78% and 80%) and (82%, 83% and 84%) respectively for real exposure of colors and for imagination of colors are (72%, 70% and 73%) and (73%, 75% and 72%) respectively. EED performed better than EER.
Another experiment was performed with different shapes of colors and the EEG data was categorized as four different groups for classification. In group1, the classification accuracies for circle, square and triangle are found to be (88%, 52%, 94%), (84%, 47%, 89%) and (84%, 49%, 94%) respectively as triplet (linear, polynomial, RBF). In group 2, 3 and 4 classification accuracies achieved are (71%, 50%, 94%), (60%, 48%, 92%) and (57%, 29%, 94%) respectively as triplet of (linear, polynomial, RBF) kernels. After the successful classification of colour information from EEG signals we are planning to work for online classification in order to implement with any possible future Brain-Computer Interface applications. We believe that this study could further be extended to find out the possibilities for e.g. simulating a scenario of traffic light signals in virtual environment or to identify and explore any possibility of analyzing the EEG signals and developing BCI applications for color blind and/or blind people. Since such applications are quite novel in their fields of BCI therefore requires extensive collaborative research work in different domains.
41
Hajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.
Abstract:
Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit en période critique (décharges). Les deux premières méthodes, qui sont fondées sur la décomposition généralisée en valeurs propres (GEVD) et sur le débruitage par séparation de sources (DSS), sont utilisées pour débruiter des signaux EEG épileptiques intercritiques. Pour extraire l'information a priori requise par GEVD et DSS, nous proposons une série d'étapes de prétraitement, comprenant la détection de pointes, l'extraction du support des pointes et le regroupement des pointes impliquées dans chaque source d'intérêt. Deux autres méthodes, appelées Temps Fréquence (TF) -GEVD et TF-DSS, sont également proposées afin de débruiter les signaux EEG critiques. Dans ce cas on extrait la signature temps-fréquence de la décharge critique par la méthode d'analyse de corrélation canonique. Nous proposons également une méthode d'Analyse en Composantes Indépendantes (ICA), appelé JDICA, basée sur une stratégie d'optimisation de type Jacobi. De plus, nous proposons un nouvel algorithme direct de décomposition canonique polyadique (CP), appelé SSD-CP, pour calculer la décomposition CP de tableaux à valeurs complexes. L'algorithme proposé est basé sur la décomposition de Schur simultanée (SSD) de matrices particulières dérivées du tableau à traiter. Nous proposons également un nouvel algorithme pour calculer la SSD de plusieurs matrices à valeurs complexes. Les deux derniers algorithmes sont utilisés pour débruiter des données intercritiques et critiques. Nous évaluons la performance des méthodes proposées pour débruiter les signaux EEG (simulés ou réels) présentant des activités intercritiques et critiques épileptiques bruitées par des artéfacts musculaires. Dans le cas des données simulées, l'efficacité de chacune de ces méthodes est évaluée d'une part en calculant l'erreur quadratique moyenne normalisée entre les signaux originaux et débruités, et d'autre part en comparant les résultats de localisation de sources, obtenus à partir des signaux non bruités, bruités, et débruités. Pour les données intercritiques et critiques, nous présentons également quelques exemples sur données réelles enregistrées chez des patients souffrant d'épilepsie partielleIn the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. Two other methods, called Time Frequency (TF)-GEVD and TF-DSS, are also proposed in order to denoise ictal EEG signals for which the time-frequency signature is extracted using the Canonical Correlation Analysis method. We also propose a deflationary Independent Component Analysis (ICA) method, called JDICA, that is based on Jacobi-like iterations. Moreover, we propose a new direct algorithm, called SSD-CP, to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. The last two algorithms are used to denoise both interictal and ictal data. We evaluate the performance of the proposed methods to denoise both simulated and real epileptic EEG data with interictal or ictal activity contaminated with muscular activity. In the case of simulated data, the effectiveness of the proposed algorithms is evaluated in terms of Relative Root Mean Square Error between the original noise-free signals and the denoised ones, number of required ops and the location of the original and denoised epileptic sources. For both interictal and ictal data, we present some examples on real data recorded in patients with a drug-resistant partial epilepsy
42
Jarjees, Mohammed Sabah. "The causality between Electroencephalogram (EEG) and Central Neuropathic Pain (CNP), and the effectiveness of neuromodulation strategies on cortical excitability and CNP in patients with spinal cord injury." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/7985/.
Abstract:
Spinal Cord Injury has primary consequences visible immediately upon injury and secondary consequence which develop some time after injury. One of the primary consequences of SCI is loss or impairment of sensory and motor functions. Related secondary consequences of the injury are Central Neuropathic Pain (CNP) and spasticity. Several studies have found that CNP can affect the cortical activity of the patient and long term CNP causes anatomical cortical changes. Therefore, early prediction and treatment of CNP could potentially prevent these changes and hopefully increase responsiveness to the treatment. Neurofeedback (NF) technique, which is a sub-category of biofeedback that uses brain waves as physiological parameters to be modulated, can be used to alter this change in cortical activity and treat CNP. The sensory motor cortex is the area of the brain responsible for voluntary control of movement and for cortical modulation of reflexes. NF provided from the sensory-motor area can therefore affect both CNP and voluntary and reflex movements. The aim of this PhD project was to explore the influence of neuromodulation strategies over the central cortex on the H reflex and CNP following SCI. It also aimed to investigate the causal relationship between the change in EEG activity and the transitional period from early symptoms of CNP to the chronic phase of CNP following SCI. The first study of this project was performed on able-bodied volunteers to explore the effect of the short-term neuromodulation strategies: NF, motor imagery (MI) and mental math (MM) of the sensory-motor rhythm (SMR) on the soleus H reflex. Results of the study showed that it is possible to achieve short-term modulation of the H reflex through short-term modulation of the SMR. Various mental tasks dominantly facilitate the H reflex irrespective of the direction of SMR modulation. The results of this study can be used to explain the effect of NF therapy on spasticity in SCI patient, for example. The second study analysed predictors of CNP in sub-acute SCI patients who have not yet developed physical symptoms of pain. It compared EEG signal between patients who did and did not develop pain within the first six months after EEG recording as well as patients with CNP and able bodied volunteers. This study demonstrated that changes in spontaneous and induced EEG can be both predictors and consequences of CNP following SCI. The third study explores the effectiveness of Neurofeedback (NF) on treatment of CNP in subacute SCI patients with CNP. The results of this study demonstrate that the NF treatment has a positive effect on the reduction of pain, at least over the period of the study. However, numerous factors, and in particular patients’ low prioritization of pain, indicate that early NF of CNP in SCI patients might not be a practical solution. The fourth study utilizes advanced methods of source analysis to define dynamic signatures of long standing CNP by using Measure Projection Analysis (MPA) for movement related cortical potential (MRCP). To separate the effect of long-term paralysis from the effect of long-term CNP, brain activity has been compared between three groups: able bodied volunteers, patients with chronic paraplegia (paralysis of lower limbs) with no pain and patients with chronic paraplegia and long standing CNP. This study showed that the movement related potential is dominantly influenced by paralysis while both CNP and paralysis affect the reafferentation component of the MRCP. Additionally, CNP influences cognitive processes in a manner that depends on the functional area of the cortex.
43
Haig, Albert Roland. "Missing Links the role of phase synchronous gamma oscillations in normal cognition and their dysfunction in schizophrenia." University of Sydney. Psychological Medicine, 2002. http://hdl.handle.net/2123/848.
Abstract:
SUMMARY Introduction: There has recently been a great deal of interest in the role of synchronous high-frequency gamma oscillations in brain function. This interest has been motivated by an increasing body of evidence, that oscillations which are synchronous in phase across separated neuronal populations, may represent an important mechanism by which the brain binds or integrates spatially distributed processing activity which is related to the same object. Many models of schizophrenia suggest an impairment in the integration of brain processing, such as a loosening of associations, disconnection, defective multiple constraint organization, or cognitive dysmetria. This has led to recent speculation that abnormalities of high-frequency gamma synchronization may reflect a core dimension of the disturbance underlying this disorder. However, examination of the phase synchronization of gamma oscillations in patients with schizophrenia has never been previously undertaken. Method: In this thesis a new method of analysis of gamma synchrony was introduced, which enables the phase relationships of oscillations in a specific frequency band to be examined across multiple scalp sites as a function of time. This enabled, for the first time, the phase synchronization of gamma oscillations across widespread regions, to be studied in electrical brain activity measured at the scalp in humans. Gamma synchrony responses were studied in electroencephalographic (EEG) data acquired during a commonly employed conventional auditory oddball paradigm. The research consisted of two sets of experiments. In the first set of experiments, data from 100 normal subjects, consisting of 10 males and 10 females in each age decade from 20 to 70, was examined. These experiments were designed to characterize the gamma synchonizations that occurred in response to target and background stimuli and their functional significance in normal brain activity, and to exclude the possibility of these findings being due to electromyogram (EMG) or volume conduction artifact. The examination of functional significance involved the development of an additional new analysis technique. In the second set of experiments, data acquired from 35 patients with schizophrenia and 35 matched normal controls was analyzed. The purpose of these experiments was to determine whether patients showed disturbances of gamma synchrony compared to controls, and to establish the relationship of any such disturbances to medication levels, symptom profiles, duration of illness, and a range of psychophysiological variables. Results: In the 100 normals, responses to target stimuli were characterized by two bursts of synchronous gamma oscillations, an early (evoked) and a late (induced) synchronization, with different topographic distributions. Only the early gamma synchronization was seen in response to background stimuli. The main variable modulating the magnitude of these gamma synchronizations from epoch to epoch was pre-stimulus EEG theta (3-7 Hz) and delta (1-3 Hz) power. Early and late gamma synchrony were also associated with N1 and P3 ERP component amplitude across epochs. Across subjects, the early gamma synchronization was associated with shorter latency of the ERP components P2, N2 and P3, smaller amplitude of N1 and P2, and smaller pre-stimulus beta power. The control analyses showed that these gamma responses were specific to a narrow frequency range (37 to 41 Hz), and were not present in adjacent frequency bands. The responses were not generated by EMG contamination or volume conduction. In the 35 patients with schizophrenia, significant abnormalities of both the early and late synchronizations were observed compared to the 35 normal controls, with distinctive topographic characteristics. In general, early gamma synchrony was increased in patients compared to controls, and late gamma synchrony was decreased. These gamma synchrony disturbances were not related to medication level or the four summed symptom profile scores (positive, negative, general and total). They were, however, associated with duration of illness, becoming less severe the longer the patient had suffered from the disorder. The disordered gamma synchrony in patients was not secondary to abnormalities in other psychophysiological variables, but appeared to represent a primary disturbance. Discussion: The early synchronization may relate to the binding of object representations in early sensory processing, or, given that a constant inter-stimulus interval was employed, may be anticipatory and related to active memory. The late response is probably involved in binding in relation to activation of the internal contextual model involved in late expectancy/contextual processing (context updating or context closure) for target stimuli. The across epochs effects may relate to whether the focus of attention immediately prior to stimulus presentation is internal or is directed at the task. The across subjects effects suggest that a larger magnitude of the early gamma synchronization might indicate that the subject maintains a more stable and less ambiguous internal representation of the environment, that reduces the complexity of input and facilitates target/background discrimination and subsequent processing. The early gamma synchronization findings in patients with schizophrenia suggest that anticipatory processing involving active memory and forward-prediction of the environment is subject to over-binding or the formation of inappropriate associations. The late synchronization disturbances may reflect a fragmentation of contextual processing, and an inability to maintain contextual models of the environment intact over time. Conclusion: This research demonstrates the potential importance of integrative network activity as indexed by gamma phase synchrony in relation to normal cognition, and the possible broad relevance of such activity in psychiatric disorders. In particular, the application in this study to patients with schizophrenia showed that an impairment of brain integrative activity (missing links) might be a key feature of this illness.
44
Jaworska, Natalia. "Electrophysiological Indices in Major Depressive Disorder and their Utility in Predicting Response Outcome to Single and Dual Antidepressant Pharmacotherapies." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22873.
Abstract:
Certain electrophysiological markers hold promise in distinguishing individuals with major depressive disorder (MDD) and in predicting antidepressant response, thereby assisting with assessment and optimizing treatment, respectively. This thesis examined resting brain activity via electroencephalographic (EEG) recordings, as well as EEG-derived event-related potentials (ERPs) to auditory stimuli and facial expression presentations in individuals with MDD and controls. Additionally, the utility of resting EEG as well as auditory ERPs (AEPs), and the associated loudness-dependence of AEPs (LDAEP) slope, were assessed in predicating outcome to chronic treatment with one of three antidepressant regimens [escitalopram (ESC); bupropion (BUP); ESC+BUP]. Relative to controls, depressed adults had lower pretreatment cortical activity in regions implicated in approach motives/positive processing. Increased anterior cingulate cortex (ACC)-localized theta was observed, possibly reflecting emotion/cognitive regulation disturbances in the disorder. AEPs and LDAEPs, putative indices of serotonin activity (implicated in MDD etiology), were largely unaltered in MDD. Assessment of ERPs to facial expression processing indicated slightly blunted late preconscious perceptual processing of expressions, and prolonged processing of intensely sad faces in MDD. Faces were rated as sadder overall in MDD, indicating a negative processing bias. Treatment responders (vs. non-responders) exhibited baseline cortical hypoactivity; after a week of treatment, cortical arousal emerged in responders. Increased baseline left fronto-cortical activity and early shifts towards this profile were noted in responders (vs. non-responders). Responders exhibited a steep, and non-responders shallow, baseline N1 LDAEP derived from primary auditory cortex activity. P2 LDAEP slopes (primary auditory cortex-derived) increased after a week of treatment in responders and decreased in non-responders. Consistent with overall findings, ESC responders displayed baseline cortical hypoactivity and steep LDAEP-sLORETA slopes (vs. non-responders). BUP responders also exhibited steep baseline slopes and high ACC theta. These results indicate that specific resting brain activity profiles appear to distinguish depressed from non-depressed individuals. Subtle ERP modulations to simple auditory and emotive processing also existed in MDD. Resting alpha power, ACC theta activity and LDAEP slopes predicted antidepressant response in general, but were limited in predicting outcome to a particular treatment, which may be associated with limited sample sizes.
45
Mileros, Martin D. "A Real-Time Classification approach of a Human Brain-Computer Interface based on Movement Related Electroencephalogram." Thesis, Linköping University, Department of Mechanical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2824.
Abstract:
A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time.
Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network.
A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.
46
Wang, Anli. "Functional significance of human sensory ERPs : insights from modulation by preceding events." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:2dcd4959-8638-4ee1-b591-3eb28bdf3a1d.
Abstract:
The electroencephalogram (EEG) reflects summated, slow post-synaptic potentials of cortical neurons. Sensory, motor or cognitive events (such as a fast-rising sensory stimulus, a brisk self-paced movement or a stimulus-triggered cognitive task) can elicit transient changes in the ongoing human EEG, called event-related potentials (ERPs). ERPs are widely used in clinical practice, and believed to reflect the activity of the sensory system activated by the stimulus (for example, laser-evoked potentials are used to substantiate the neuropathic nature of clinical pain conditions). When ERPs are elicited by pairs or trains of stimuli delivered at short inter-stimulus intervals (ISIs), the magnitude of the ERP elicited by the repeated stimuli is markedly reduced, a phenomenon known as response decrement. While the interval between two consecutive stimuli becomes longer, the reduced response is recovered. Thus, this phenomenon has been traditionally interpreted in terms of neural refractoriness of generators of ERPs ("neural refractoriness hypothesis"). This thesis, however, challenges this neural refractoriness hypothesis by describing the results of manipulating the preceding events of the eliciting stimulus. The first study examined the effect of variable and short ISIs on sensory ERPs, delivering trains of auditory and electrical stimuli with random ISIs ranging from 100 to 1000ms. In the second study, pairs of laser stimuli were presented in two comparable conditions. In the constant condition, the ISI was identical across trials in each block, while in the variable condition, the ISI was variable across trials. By directly comparing ERPs elicited by laser stimulation, this study aimed to explore whether lack of saliency in the eliciting stimulus could explain the response decrement during stimulus repetition. Finally, the third study tested the hypothesis that the reduced eliciting ERPs would recover if saliency were introduced by changing the modality of the preceding event. Thus, trains of three stimuli (S1-S2-S3) with 1s ISI were presented; S2 was either same or different in modality as S1 and S3 in each block. Results from these three experiments demonstrate that this "refractoriness hypothesis" does not hold, and suggest that the magnitude of ERPs is only partly related to the magnitude of the incoming sensory input, and instead largely reflects neural activities triggered by salient events in the sensory environment. These results are important for the correct interpretation of ERPs in both physiological and clinical studies.
47
Ta, Christopher Ian. "Interfacing a Brain Control Interface towards the Development of a Retrofitted, Low-Cost, Open Sourced, Electric Wheelchair." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1707240/.
Abstract:
The Emotiv Insight is a commercially available, low-cost, mobile EEG device that is commonly overshadowed by its costlier counterpart, the Emotiv EPOC. The purpose of this report is to investigate if the Emotiv Insight is a suitable headset to be used as a controlling factor in conjunction with an Arduino microcontroller and various electrical components that are used towards the development of an open-sourced, affordable electric wheelchair with the primary goal of providing those who either do not have the financial resources or the physical capability to operate a traditional wheelchair due to their disability a viable option to improve their quality of life. All of the C++ code, STL files used to fabricate the 3d-printed components are uploaded to a GitHub repository as open sourced files to allow individuals with access to a 3d-printer to either build the open sourced wheelchair for their personal use, or refine the design to suit their needs.
48
Azami, Hamed. "Entropy-based nonlinear analysis for electrophysiological recordings of brain activity in Alzheimer's disease." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31106.
Abstract:
Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells causes memory loss and cognitive decline. As AD progresses, changes in the electrophysiological brain activity take place. Such changes can be recorded by the electroencephalography (EEG) and magnetoencephalography (MEG) techniques. These are the only two neurophysiologic approaches able to directly measure the activity of the brain cortex. Since EEGs and MEGs are considered as the outputs of a nonlinear system (i.e., brain), there has been an interest in nonlinear methods for the analysis of EEGs and MEGs. One of the most powerful nonlinear metrics used to assess the dynamical characteristics of signals is that of entropy. The aim of this thesis is to develop entropy-based approaches for characterization of EEGs and MEGs paying close attention to AD. Recent developments in the field of entropy for the characterization of physiological signals have tried: 1) to improve the stability and reliability of entropy-based results for short and long signals; and 2) to extend the univariate entropy methods to their multivariate cases to be able to reveal the patterns across channels. To enhance the stability of entropy-based values for short univariate signals, refined composite multiscale fuzzy entropy (MFE - RCMFE) is developed. To decrease the running time and increase the stability of the existing multivariate MFE (mvMFE) while keeping its benefits, the refined composite mvMFE (RCmvMFE) with a new fuzzy membership function is developed here as well. In spite of the interesting results obtained by these improvements, fuzzy entropy (FuzEn), RCMFE, and RCmvMFE may still lead to unreliable results for short signals and are not fast enough for real-time applications. To address these shortcomings, dispersion entropy (DispEn) and frequency-based DispEn (FDispEn), which are based on our introduced dispersion patterns and the Shannon’s definition of entropy, are developed. The computational cost of DispEn and FDispEn is O(N) – where N is the signal length –, compared with the O(N2) for popular sample entropy (SampEn) and FuzEn. DispEn and FDispEn also overcome the problem of equal values for embedded vectors and discarding some information with regard to the signal amplitudes encountered in permutation entropy (PerEn). Moreover, unlike PerEn, DispEn and FDispEn are relatively insensitive to noise. As extensions of our developed DispEn, multiscale DispEn (MDE) and multivariate MDE (mvMDE) are introduced to quantify the complexity of univariate and multivariate signals, respectively. MDE and mvMDE have the following advantages over the existing univariate and multivariate multiscale methods: 1) they are noticeably faster; 2) MDE and mvMDE result in smaller coefficient of variations for synthetic and real signals showing more stable profiles; 3) they better distinguish various states of biomedical signals; 4) MDE and mvMDE do not result in undefined values for short time series; and 5) mvMDE, compared with multivariate multiscale SampEn (mvMSE) and mvMFE, needs to store a considerably smaller number of elements. In this Thesis, two restating-state electrophysiological datasets related to AD are analyzed: 1) 148-channel MEGs recorded from 62 subjects (36 AD patients vs. 26 age-matched controls); and 2) 16-channel EEGs recorded from 22 subjects (11 AD patients vs. 11 age-matched controls). The results obtained by MDE and mvMDE suggest that the controls’ signals are more and less complex at respectively short (scales between 1 to 4) and longer (scales between 5 to 12) scale factors than AD patients’ recordings for both the EEG and MEG datasets. The p-values based on Mann-Whitney U-test for AD patients vs. controls show that the MDE and mvMDE, compared with the existing complexity techniques, significantly discriminate the controls from subjects with AD at a larger number of scale factors for both the EEG and MEG datasets. Moreover, the smallest p-values are achieved by MDE (e.g., 0.0010 and 0.0181 for respectively MDE and MFE using EEG dataset) and mvMDE (e.g., 0.0086 and 0.2372 for respectively mvMDE and mvMFE using EEG dataset) for both the EEG and MEG datasets, illustrating the superiority of these developed entropy-based techniques over the state-of-the-art univariate and multivariate entropy approaches. Overall, the introduced FDispEn, DispEn, MDE, and mvMDE methods are expected to be useful for the analysis of physiological signals due to their ability to distinguish different types of time series with a low computation time.
49
Gomes, Rodrigo San Martin Ignacio. "Avaliação do filtro sensório-motor através de registro de eletroencefalograma (EEG) e teste de inibição pré-pulso (IPP) em pacientes após primeiro episódio psicótico." reponame:Repositório Institucional da UFABC, 2017.
Abstract:
Orientadora: Profa. Dra. Cristiane Otero Reis SalumCoorientador: Prof. Dr. Francisco José Fraga da Silva
Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, São Bernardo do Campo, 2017.
Pacientes de transtorno bipolar e esquizofrenia apresentam déficits no processamento de informação. Dentre esses déficits está uma disfunção do mecanismo de filtragem sensorial, que pode ser observada através do teste de Inibição Pré-Pulso (IPP), que acessa a inibição das respostas muscular, observada por eletromiografia (EMG) e neural, observada por eletroencefalograma (EEG) através da inibição de potenciais evocados, como o P2-N1. No fenômeno da IPP, é observado que a resposta iniciada por um estímulo de alta intensidade é reduzida quando este é precedido em alguns milissegundos (30-300ms) por outro estímulo de baixa intensidade. Esses estímulos são respectivamente chamados de Pulso (P) e Pré-Pulso (PP). A porcentagem de redução da resposta ao P, quando este é precedido por um PP é calculada em relação à magnitude de resposta que seria evocada pelo P quando este não é precedido por PP algum. O presente estudo visou avaliar o filtro sensorial através do registro simultâneo dos sinais eletromiográficos e eletroencefalográficos em pacientes brasileiros de primeiro episódio psicótico de transtorno bipolar (BP) e esquizofrenia (SZ). Vinte pacientes BP, quinze pacientes SZ e 22 sujeitos sadios participaram do estudo. Pacientes SZ apresentam redução da %IPP observada por EMG em relação a pessoas sadias, ao passo que pacientes do grupo BP não apresentam redução da filtragem sensório-motora. Para a IPP neural, foi observada redução na amplitude de P do grupo BP na região frontal, avaliada pelo eletrodo Fz e redução da amplitude de P e também na %IPP para os grupos BP e SZ na região parietal, avaliada pelo eletrodo Pz. Os resultados indicam que a redução da filtragem sensorial foi observada em diferentes estágios do processamento sensorial. E a divergência entre IPP clássica e IPP neural para o grupo BP sugere que a IPP medida por EMG clássica e medida por EEG refletem filtros sensoriais diferentes e que pacientes de diferentes grupos podem exibir déficits em um desses filtros apenas. O presente trabalho é o pioneiro na utilização de ferramentas de atenuação de artefatos contaminantes do sinal neural no teste de IPP neural.
Patients with bipolar disorder and schizophrenia have deficits in information processing. Among these deficits is a dysfunction of the sensory filtering mechanism, which can be observed through the Prepulse Inhibition (PPI) test, which accesses the inhibition of muscle responses, observed by electromyography (EMG) and neural, observed by electroencephalogram (EEG) through inhibition of evoked potentials, such as P2-N1. In the PPI phenomenon, it is observed that the response initiated by a high intensity stimulus is reduced when it is preceded in a few milliseconds (30-300ms) by another low intensity stimulus. These stimuli are respectively called Pulse (P) and Prepulse (PP). The reduction percentage of the response to P when it is preceded by a PP is calculated in relation to the magnitude of response that would be evoked by P when it is not preceded by any PP. The present study aimed to evaluate the sensory filter through the simultaneous recording of electromyographic and electroencephalographic signals in Brazilian patients with first psychotic episode of bipolar disorder (BP) and schizophrenia (SZ). Twenty BP patients, fifteen SZ patients and 22 healthy subjects participated in the study. SZ patients presented a reduction in the %PPI observed by EMG when compared to healthy individuals, whereas patients in the BP group did not show reduction of sensory-motor filter. For the neural PPI, a reduction in BP group P amplitude was observed in the frontal region, evaluated by the Fz electrode. Also, was observed a reduction in the P amplitude and in the %PPI for the BP and SZ groups in the parietal region, evaluated by the Pz electrode. These results indicate that the reduction of sensorial filtration was observed at different stages of sensorial processing. And the divergence between classical IPP and neural IPP for the BP group suggests that PPI measured by classical EMG and measured by EEG reflect different sensory filters and that patients from different groups may exhibit deficits in one of these filters only. The present work is the pioneer in the use of attenuation tools to reduce contaminating artifacts in PPI test neural signal.
50
Kawaguchi, Hirokazu. "Signal Extraction and Noise Removal Methods for Multichannel Electroencephalographic Data." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/188593.