Dissertations / Theses on the topic 'Electroencephalogram (EEG)'
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Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
Full textDuta, 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.
Full textLee, Pamela Wen-Hsin. "Mutual information derived functional connectivity of the electroencephalogram (EEG)." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/219.
Full textMathew, Blesy Anu. "ENTROPY OF ELECTROENCEPHALOGRAM (EEG) SIGNALS CHANGES WITH SLEEP STATE." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/203.
Full textRiddington, Edward Peter. "Automated interpretation of the background EEG using fuzzy logic." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1109.
Full textD'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.
Full textLöfhede, 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.
Full textMurrell, 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.
Full textTcheslavski, Gleb V. "Coherence and Phase Synchrony Analysis of Electroencephalogram." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/30186.
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Ascolani, Gianluca. "EEG, Alpha Waves and Coherence." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc28389/.
Full textHolt, 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.
Full textRoessgen, 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.
Full textRodriguez, 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.
Full textEncephalogram (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.
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.
Full textBCI 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.
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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.
Full textWu, 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.
Full textA 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.
Qassim, Yahya Taher. "FPGA Design and Implementation of Wavelet Coherence for EEG Signals." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/366086.
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Doctor of Philosophy (PhD)
School of Engineering
Science, Environment, Engineering and Technology
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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/.
Full textVennelaganti, Swetha. "AGING AND SLEEP STAGE EFFECTS ON ENTROPY OF ELECTROENCEPHALOGRAM SIGNALS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/553.
Full textAlhajjar, 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.
Full textThe 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
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.
Full textSadovský, Petr. "Analýza spánkového EEG." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2007. http://www.nusl.cz/ntk/nusl-233411.
Full textHassanpour, 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.
Full textHassanpour, Hamid. "Time-Frequency Based Detection of Newborn EEG Seizure." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15853/.
Full textNussbaum, Paul. "Signal Processing of Electroencephalogram for the Detection of Attentiveness towards Short Training Videos." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/558.
Full textMontgomery, 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.
Full textSegalotto, 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.
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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.
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.
Full textHERATH, 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.
Full textMahadevan, 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.
Full textLoughran, 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.
Full textZarjam, 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/.
Full textZarjam, 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.
Full textCHIKUSHI, 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.
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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.
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.
Full textPrice, 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.
Find full textRankine, 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.
Full textRankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16200/.
Full textJaneček, 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.
Full textRasheed, 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.
Full textHajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.
Full textIn 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
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/.
Full textHaig, 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.
Full textJaworska, 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.
Full textMileros, 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.
Full textA 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.
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.
Full textTa, 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/.
Full textAzami, 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.
Full textGomes, 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.
Find full textCoorientador: 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.
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.
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