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Zhang, Shuoyue [Verfasser], and Jürgen [Akademischer Betreuer] Hennig. "Artifacts denoising of EEG acquired during simultaneous EEG-FMRI." Freiburg : Universität, 2021. http://d-nb.info/1228786968/34.
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Balli, Tugce. "Nonlinear analysis methods for modelling of EEG and ECG signals." Thesis, University of Essex, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528852.
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JESSY, PAROKARAN. "Analysis of EEG Signals for EEG-based Brain-Computer Interface." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-6622.
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Advancements in biomedical signal processing techniques have led Electroencephalography (EEG) signals to be more widely used in the diagnosis of brain diseases and in the field of Brain Computer Interface(BCI). BCI is an interfacing system that uses electrical signals from the brain (eg: EEG) as an input to control other devices such as a computer, wheel chair, robotic arm etc. The aim of this work is to analyse the EEG data to see how humans can control machines using their thoughts.In this thesis the reactivity of EEG rhythms in association with normal, voluntary and imagery of hand movements were studied using EEGLAB, a signal processing toolbox under MATLAB. In awake people, primary sensory or motor cortical areas often display 8-12 Hz EEG activity called ’Mu’ rhythm ,when they are not engaged in processing sensory input or produce motor output. Movement or preparation of movement is typically accompanied by a decrease in this mu rhythm called ’event-related desynchronization’(ERD). Four males, three right handed and one left handed participated in this study. There were two sessions for each subject and three possible types : Imagery, Voluntary and Normal. The EEG data was sampled at 256Hz , band pass filtered between 0.1 Hz and 50 Hz and then epochs of four events : Left button press , Right button press, Right arrow ,Left arrow were extracted followed by baseline removal.After this preprocessing of EEG data, the epoch files were studied by analysing Event Related Potential plots, Independent Component Analysis, Power spectral Analysis and Time-Frequency plots. These analysis have shown that an imagination or a movement of right hand cause a decrease in activity in the hand area of sensory motor cortex in the left side of the brain which shows the desynchronization of Mu rhythm and an imagination or a movement of left hand cause a decrease in activity in the hand area of sensory motor cortex in the right side of the brain. This implies that EEG phenomena may be utilised in a Brain Computer Interface operated simply by motor imagery and the present result can be used for classifier development and BCI use in the field of motor restoration
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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.
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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.
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Caat, Michael ten. "Multichannel EEG visualization." [S.l. : Groningen : s.n. ; University Library of Groningen] [Host], 2008. http://irs.ub.rug.nl/ppn/306087987.
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Congedo, Marco. "EEG Source Analysis." Habilitation à diriger des recherches, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00880483.
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Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families, namely 1) EEG distributed inverse solutions, 2) diagonalization methods, including spatial filtering and blind source separation and 3) Riemannian geometry. We highlight our contributions in each of this family, we describe algorithms reporting all necessary information to make purposeful use of these methods and we give numerous examples with real data pertaining to our published studies. Traditionally only the single-subject scenario is considered; here we consider in addition the extension of some methods to the simultaneous multi-subject recording scenario. This HDR can be seen as an handbook for EEG source analysis. It will be particularly useful to students and other colleagues approaching the field.
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Lovelace, Joseph A. "Ambulatory EEG Platform." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479816584544204.
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Holdova, Kamila. "Klasifikace spánkových EEG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219944.
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This thesis deals with wavelet analysis of sleep electroencephalogram to sleep stages scoring. The theoretical part of the thesis deals with the theory of EEG signal creation and analysis. The polysomnography (PSG) is also described. This is the method for simultaneous measuring the different electrical signals; main of them are electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG). This method is used to diagnose sleep failure. Therefore sleep, sleep stages and sleep disorders are also described in the present study. In practical part, some results of application of discrete wavelet transform (DWT) for decomposing the sleep EEGs using mother wavelet Daubechies 2 „db2“ are shown and the level of the seven. The classification of the resulting data was used feedforward neural network with backpropagation errors.
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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.
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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.
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Chowdhury, Muhammad Enamul Hoque. "Simultaneous EEG-fMRI : novel methods for EEG artefacts reduction at source." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14297/.
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This thesis describes the development and application of novel techniques to reduce the EEG artefacts at source during the simultaneous acquisition of EEG and fMRI data. The work described in this thesis was carried out by the author in the Sir Peter Mansfield Magnetic Resonance Centre, School of Physics & Astronomy at the University of Nottingham, between October 2010 and January 2013. Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after correction, which can easily swamp signals from brain activity. Therefore any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, and facilitating improved detection of brain activity. This thesis firstly explores a new method for reducing the gradient artefact (GA), which is induced in EEG data recorded during concurrent MRI, by investigating the effects of the cable configuration on the characteristics of the GA. This work showed that the GA amplitude and its sensitivity to movement of the cabling is reduced by minimising wire loop areas in the cabling between the EEG cap and amplifier. Another novel approach for reducing the magnitude and variability of the artefacts is the use of an EEG cap that incorporates electrodes embedded in a reference layer, which has a similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads are theoretically similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Therefore taking the difference of the voltages in the reference and scalp channels should reduce the artefacts, without affecting sensitivity to neuronal signals. The theoretical efficacy of artefact correction that can be achieved by using this new reference layer artefact subtraction (RLAS) method was investigated. This was done through separate electromagnetic simulations of the artefacts induced in a hemispherical reference layer and a spherical volume conductor in a time-varying magnetic field and the results showed that similar artefacts are induced on the surface of both conductors. Simulations are also performed to find the optimal design for an RLAS system, by varying the geometry of the system. A simple experimental realisation of the RLAS system was implemented to investigate the degree of artefact attenuation that can be achieved via RLAS. Through a series of experiments on phantoms and human subjects, it is shown here that RLAS significantly reduces the GA, pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms the standard artefact correction method, average artefact subtraction (AAS), in the removal of the GA and PA when motion is present, while the combination of RLAS and AAS always produces higher artefact attenuation than AAS alone. Additionally, this work demonstrates that RLAS greatly attenuates the unpredictable and highly variable MA that are very hard to remove using post-processing methods.
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Mechau, Dorothee. "EEG im Sport : kortikale Aktivität im topographischen EEG durch sportliche Beanspruchung /." Schorndorf : Hofmann, 2001. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=009706162&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Ištok, Martin. "Analýza simultánně měřených EEG/fMRI dat s využitím zpracování EEG signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221335.
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The main objective of this diploma thesis is to describe simultaneous electroencephalography-correlated functional magnetic resonance imaging analysis using EEG data processing. It includes basic characteristics of EEG and fMRI recording and analysis and their combination as simultaneous EEG/fMRI analysis and deals with obstructions during its processing. The thesis includes a design of an experiment used for recording and analysis of simultaneous EEG/fMRI data using EEG source reconstruction for regressor construction. Thesis incorporates a software solution used for extraction of signal describing a source activity interpolated by EEG source reconstruction. The signal is then processed and used to construct a basic regressor. The thesis also deals with the software solution being used for a study focused on intracranial epileptic discharges localization using a simultaneous EEG/fMRI analysis in which it reveals source activity during ongoing epileptic spike and summarizes the results.
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Mayhew, Stephan D. "Single-trial EEG analysis and its application to simultaneous EEG and fMRI." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497049.
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Frohne, Ursula. "EEG-Grundaktivität und Intelligenz." Diss., lmu, 2002. http://nbn-resolving.de/urn:nbn:de:bvb:19-7435.
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Kanerva, Peter, and Hampus Karlberg. "Application Control Using EEG." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200570.
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Sato, Diogo Mululo. "EEG Analysis by Compression." Master's thesis, Faculdade de Medicina da Universidade do Porto, 2011. http://hdl.handle.net/10216/63767.
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Zapletal, Jakub. "Aplikace pro EEG biofeedback." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449178.
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Tato práce je shrnutím existujících přístupů pro zpracování EEG signálu za účelem EEG biofeedbacku a dále popisuje návrh a implementaci vlastní aplikace pro EEG biofeedback se zaměřením na trénink pozornosti. Dále obsahuje případovou studii provedenou na neurotypickém studentovi a studentovi s ADHD, která zkoumá vliv implementované aplikace na měřený EEG signál subjektů.
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Sato, Diogo Mululo. "EEG Analysis by Compression." Dissertação, Faculdade de Medicina da Universidade do Porto, 2011. http://hdl.handle.net/10216/63767.
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Ježek, Martin. "Analýza spánkového signálu EEG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217961.
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Cílem této práce byl vývoj programu pro automatickou detekci arousalu v signálu spánkového EEG s použitím metod časově-frekvenční analýzy. Předmětem studie bylo 13 celonočních polysomnografických nahrávek (čtyři svody EEG, EMG, EKG a EOG), tj. celkově více než 100 hodin záznamu. Jednalo se o část dat z dřívějších výzkumných prací expertní lékařky v problematice spánku Dr. Emilie Sforzy, Ženeva, Švýcarsko, která rovněž poskytla základní hodnocení těchto dat. V záznamech bylo celkem označeno 1551 arousal událostí. Pro usnadnění výběru konkrétní metody časově-frekvenční analýzy byla následně vytvořena sada nástrojů pro vizualizaci jednotlivých signálů a jejich různých časově-frekvenčních vyjádření. S ohledem na závěry vizuální analýzy, charakter signálu EEG a efektivitu výpočetních metod byla pro analýzu vybrána waveletová transformace s mateřskou vlnkou Daubechies řádu 6. Jednotlivé svody EEG byly dekomponovány do šesti frekvenčních pásem. Z takto odvozených signálů a signálu EMG byly následně stanoveny ukazatele možné přítomnosti události arousalu. Tyto ukazatele byly dále váhovány lineárním klasifikátorem, jehož hodnoty vah byly optimalizovány pomocí genetického algoritmu. Na základě hodnoty lineárního klasifikátoru bylo rozhodnuto o přítomnosti události arousalu v daném svodě EEG – arousal byl detekován, jestliže hodnota klasifikátoru překročila danou mez na dobu více než 3 a méně než 30 vteřin. V celém záznamu pak byl arousal označen, byl-li detekován alespoň v jednom ze svodů EEG. Následně byly odvozeny míry senzitivity a selektivity detekce, jež byly rovněž základem pro stanovení fitness funkce genetického algoritmu. Pro učení genetického algoritmu byly vybrány první čtyři záznamy. Na základě takto optimalizovaných vah vznikl program pro automatickou detekci, který na celém souboru 13 záznamů dosáhl ve srovnání s expertním hodnocením míry senzitivity 76,09%, selektivity 53,26% a specificity 97,66%.
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Ronzhina, Marina. "Klasifikace mikrospánku analýzou EEG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217965.
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This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.
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Blatný, Michal. "Spektrální analýza EEG signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219237.
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Master’s thesis deal about electroencefalography, measurement EEG signals and analysis measuermed signals. Project contains two basis practical parts. Firts part contain two PC’s programs that’s are used to fundamental analysis to frequence-domain and visual display of brain mapping created with Matlab. Second chapter of practical parts includes two PC’s programs created with LabView. First of them is the EEG biofeedback making use for advanced analyses and second program is used to detection segment of stacionarity.
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Hodulíková, Tereza. "Analýza EEG během anestezie." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220369.
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This master's thesis deals with the method of functional examination of brain electric activity. In the first part is description of central nervous system, method of electroencephalography and possible connections. Furthermor the project involves characteristic of EEG signal and its artifacts. It also includes signal processing and list of symptoms, which will be used for an analysis of the EEG during anesthesia. The second part of thesis involves development of application, which allow viewing and proccesing of EEG signal. In conclusion of thesis is carried out unequal segmentation and statistical processing.
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Fuchs, Bernhard [Verfasser]. "Integrierte Sensorschaltungen zur EKG- und EEG-Ableitung mit prädiktiver Signalverarbeitung / Bernhard Fuchs." Aachen : Shaker, 2004. http://d-nb.info/1172609055/34.
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Klus, Christina Carla Paula [Verfasser], and Julia [Akademischer Betreuer] Jacobs-LeVan. "Hochfrequenzoszillationen im Oberflächen-EEG: simultane Ableitung von Hochfrequenzoszillationen im intrakraniellen und Oberflächen-EEG." Freiburg : Universität, 2019. http://d-nb.info/1196006156/34.
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Jmail, Nawel. "Séparation des activités cérébrales phasiques et oscillatoires en MEG, EEG et EEG intracérébral." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM5013/document.
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Les oscillations jouent un rôle de premier plan dans la mise en place des réseaux cérébraux sains et pathologiques. En particulier, au niveau clinique, les activités oscillatoires sont d'une grande importance diagnostique en épilepsie. Par ailleurs, les méthodes non-invasives d'électrophysiologie sont particulièrement adaptées pour la compréhension des réseaux cérébraux à grande échelle. Cependant, la majorité des études en épilepsie a été dirigée vers les pointes intercritiques, qui sont des activités transitoires. Une question qui reste donc en suspens est le lien entre les pointes épileptiques et les activités oscillatoires épileptiques. Cette thèse a visé à résoudre deux problématiques complémentaires autour de cette question. La première problématique est la séparation adéquate entre les activités oscillatoires et transitoires. Il s'agit d'une tâche difficile surtout lors d'un grand chevauchement temporel, qui peut résulter en la contamination d'une activité par l'autre. Nous avons évaluée trois méthodes de filtrage : le filtre FIR (méthode classique), la transformé d'ondelette stationnaire et le filtrage parcimonieux par matching pursuit (MP, basé sur un dictionnaire). Sur des simulations, la SWT a donné de très bons résultats pour la reconstruction des transitoires et le MP pour les oscillations ; de plus, les deux méthodes ont donné un faible taux de faux positifs en détection automatique des oscillations. La SWT et le FIR ont donné les meilleurs résultats de filtrage sur les signaux réels, en particulier lors de la localisation de sourceThe Oscillatory activities play a leading role in the development of healthy and pathological brain networks. In particular, at the clinical level, the oscillatory activities are of great importance in the diagnostic of epilepsy. In addition, the non-invasive electrophysiology methods are particularly suitable for understanding the large-scale brain networks. However, most studies in epilepsy have been directed to the interictal spikes, which are transitional activities. One issue that remains unresolved is the relationship between epileptic spikes and epileptic oscillatory activities. This thesis resolves two complementary problems. The first one is the suitable separation between the oscillatory and transitory activity, which is quite sensitive to the presence of the overlap in the time-frequency domain. This can lead to a contamination between the activities. We did evaluate three filtering methods: the FIR (classic methods), the stationary wavelet SWT and the parsimonious filter with the matching pursuit MP. The SWT gave good results in the reconstruction of transient activity and the MP in the reconstruction of oscillatory activity both for simulated data; also they provide a low false positive in automatic detection of oscillatory activity. The SWT and FIR gave the best results on real signals especially for source localization. In the simulated data, the MP is optimal since the atoms of the dictionary resembles to the simulated signals, which isn't guaranteed for real signals. The second problem is the comparison between network connectivity of transient and oscillatory activity, as measured in surface recordings (MEG) and invasive recordings SEEG
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Mazzonetto, Ilaria. "EEG source reconstruction accuracy and integration of simultaneous EEG-fMRI resting state data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3422668.
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The resting state functional magnetic resonance imaging (fMRI) approach has allowed to investigate the large scale organization of processing systems in the human brain, revealing that it can be viewed as an integrative network of functionally interacting regions. However, to date the neuronal basis of the fluctuations of the fMRI signal at rest are not fully understood, preventing the possibility to elucidate their functional role. In this scenario, the integration with information derived from electroencephalography (EEG) is very useful, since conversely from fMRI, EEG represents a direct measure of neuronal activity. EEG-fMRI resting state studies investigating the correlation between fMRI signals and corresponding global EEG spectral characteristics in single spectral bands have provided a certain degree of inconsistency in the results. This may be due to the fact that the distinct functional networks involve more than a single frequency band and therefore analysis of simultaneous EEG/fMRI data should consider the whole frequency spectrum. A couple of studies have been performed in this directions but they either did not investigate how the scalp distribution of the EEG spectral metrics affects the patterns of correlations between EEG spectral dynamics and fMRI-derived resting state network or did not identify the specific scalp regions that specifically determined the pattern of observed results. To overcome this gap, with the aim to identify specific spatio-spectral fingerprints of distinct networks, a first study was conducted using an analytical approach that allows to take into account the interplay between the different EEG frequency bands and the corresponding topographic distribution within each network. Specifically, this approach was applied to four sub-components of the Default Mode Network (DMN). Results revealed for the first time the presence of distinctive subcomponent-specific spatial-frequency patterns of correlation between the fMRI signal and EEG rhythm. It should however be noted that spatial resolution of the EEG signal is too low to reliably infer about the location of the involved EEG sources. Therefore, a further step forward could be to try extending the findings of the first study in this direction by performing a source estimation study. Since it is not clear whether the 64 channels EEG system employed in the first study can provide adequate localization performance as regard our regions of interest, an investigation of the source reconstruction accuracy throughout the brain was performed in a second study. Specifically, the 64-channel montage was compared to 32-channel montage, the standard in the clinical practice, as well as to 128-channel montage and to 256- channel montage, considered as the upper reference point. Unlike previous studies, source performances were evaluated all over the cortical grey matter. Results indicate that the localization of the cortical sources of the spatio-spectral fingerprints revealed by the previous study can be adequately inferred by using 64 channels, but a confirmation study with a 128, or even better 256, channels montage is needed. Moreover, particular attention should be paid to investigate deep regions, where localization performance is worse regardless the number of electrodes used.Gli studi di risonanza magnetica funzionale (fMRI) in resting state hanno permesso di studiare l'organizzazione del cervello umano su ampia scala, rivelando che esso può essere visto come una rete di regioni funzionalmente connesse (networks). Ad oggi, però, le basi neurali delle fluttuazioni del segnale fMRI nelle varie regioni nella condizione di resting non sono pienamente comprese e ciò impedisce di chiarire il loro ruolo funzionale. In questo scenario, l'integrazione con l'informazione derivata dall'elettroencefalografia (EEG) è molto utile poiché questa,contrariamente alla risonanza magnetica funzionale, fornisce una misura diretta dell'attività neuronale. Finora, gli studi EEG-fMRI in condizioni di riposo che valutano le correlazioni fra il segnale fMRI e le caratteristiche spettrali del segnale EEG in una singola banda di interesse hanno portato a risultati tra loro incosistenti. Questo può essere dovuto al fatto che network funzionalmente distinti possono coinvolgere più di una singola banda, e quindi andrebbe analizzato l'intero spettro delle frequenze. Alcuni studi sono stati condotti in questa direzione ma o non hanno studiato come la distribuzione delle frequenze sullo scalpo influenza i pattern di correlazioni, o non hanno individuato quali regioni dello scalpo determinano in maniera specifica il pattern dei risultati osservati. Per superare questo limite, con lo scopo di identificare gli specifici correlati spazio-spettrali dei vari networks, un primo studio è stato condotto usando un approccio analitico che permette di considerare la relazione tre le differenti bande di frequenza EEG e la corrispondente distribuzione topografica all'interno di ciascun network. Specificatamente, questo approccio è stato applicato a quattro sottocomponenti del Default Mode Network. I risultati hanno rilevato per la prima volta la presenza di specifici pattern spazio-spettrali di correlazioni tra il segnale fMRI di un network e i diversi ritmi EEG. Dato che la risoluzione spaziale dell'EEG non permette di fare precise inferenze sulla localizzazione spaziale delle sorgenti neurali corrispondenti, un ulteriore passo in avanti potrebbe essere quello di estendere questi risultati con uno studio di ricostruzione delle sorgenti corticali. Inoltre, visto che non è chiaro se il sistema EEG a 64 canali utilizzato nel primo studio possa fornire performance accettabili, è stato fatto un secondo studio volto a valutare l’adeguatezza di questo sistema allo scopo. Nello specifico, l'accuratezza nel localizzare le sorgenti EEG ottenuta con il montaggio a 64 canali è stata confrontata con quelle ottenute con montaggi a 32 canali, lo standard in clinica, a 128 e a 256 canali. Diversamente da studi precedenti, le performance sono state valutate su tutto lo scalpo. I risultati indicano che le sorgenti corticali dei correlati spazio-spettrali dei network individuati nello studio precedente possono essere localizzate con una risoluzione spaziale adeguata usando 64 canali, sebbene sia necessario uno studio confermativo con 128 o 256 canali. Inoltre, andrebbe prestata particolare attenzione nel caso vengano investigate regioni cerebrali più profonde, nelle queli le performance sono basse a prescindere dal numero di canali utilizzato.
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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/.
Full textAbstract:
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.
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Bischoff, Matthias [Verfasser]. "Neurofunctional correlates of audiovisual binding in fMRI, EEG and EEG-guided fMRI / Matthias Bischoff." Gießen : Universitätsbibliothek, 2014. http://d-nb.info/1068589051/34.
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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.
Full textAbstract:
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.
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Chen, Tsai Yuan. "Network Electrophysiology Sensor-On-A- Chip." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-dissertations/389.
Full textAbstract:
"
Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG) bio-potential signals are commonly recorded in clinical practice. Typically, patients are connected to a bulky and mains-powered instrument, which reduces their mobility and creates discomfort. This limits the acquisition time, prevents the continuous monitoring of patients, and can affect the diagnosis of illness. Therefore, there is a great demand for low-power, small-size, and ambulatory bio-potential signal acquisition systems.
Recent work on instrumentation amplifier design for bio-potential signals can be broadly classified as using one or both of two popular techniques: In the first, an AC-coupled signal path with a MOS-Bipolar pseudo resistor is used to obtain a low-frequency cutoff that passes the signal of interest while rejecting large dc offsets. In the second, a chopper stabilization technique is designed to reduce 1/f noise at low frequencies. However, both of these existing techniques lack control of low-frequency cutoff.
This thesis presents the design of a mixed- signal integrated circuit (IC) prototype to provide complete, programmable analog signal conditioning and analog-to-digital conversion of an electrophysiologic signal. A front-end amplifier is designed with low input referred noise of 1 uVrms, and common mode rejection ratio 102 dB. A novel second order sigma-delta analog- to-digital converter (ADC) with a feedback integrator from the sigma-delta output is presented to program the low-frequency cutoff, and to enable wide input common mode range of ¡Ãƒâ€œ0.3 V. The overall system is implemented in Jazz Semiconductor 0.18 um CMOS technology with power consumption 5.8 mW from ¡Ãƒâ€œ0.9V power supplies. "
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Good, Peter Antony. "EEG beta activity in migraine." Thesis, Coventry University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316698.
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Szafir, Daniel J. "Non-Invasive BCI through EEG." Thesis, Boston College, 2010. http://hdl.handle.net/2345/1208.
Full textAbstract:
Thesis advisor: Robert SignorileIt has long been known that as neurons fire within the brain they produce measurable electrical activity. Electroencephalography (EEG) is the measurement and recording of these electrical signals using sensors arrayed across the scalp. Though there is copious research in using EEG technology in the fields of neuroscience and cognitive psychology, it is only recently that the possibility of utilizing EEG measurements as inputs in the control of computers has emerged. The idea of Brain-Computer Interfaces (BCIs) which allow the control of devices using brain signals evolved from the realm of science fiction to simple devices that currently exist. BCIs naturally present themselves to many extremely useful applications including prosthetic devices, restoring or aiding in communication and hearing, military applications, video gaming and virtual reality, and robotic control, and have the possibility of significantly improving the quality of life of many disabled individuals. However, current BCIs suffer from many problems including inaccuracies, delays between thought, detection, and action, exorbitant costs, and invasive surgeries. The purpose of this research is to examine the Emotiv EPOC© System as a cost-effective gateway to non-invasive portable EEG measurements and utilize it to build a thought-based BCI to control the Parallax Scribbler® robot. This research furthers the analysis of the current pros and cons of EEG technology as it pertains to BCIs and offers a glimpse of the future potential capabilities of BCI systems
Thesis (BA) — Boston College, 2010
Submitted to: Boston College. College of Arts and Sciences
Discipline: Computer Science Honors Program
Discipline: Computer Science
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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 textAbstract:
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.
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Barth, Alexander. "Intuitive Risikowahrnehmung eine EEG-Studie /." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-73892.
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Ryan, David B., Mark A. Eckert, Eric W. Sellers, Kim S. Schairer, and Sherri L. Smith. "EEG Study of Effortful Listening." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/1805.
Full textAbstract:
Adults with hearing loss typically experience difficulty understanding speech and report increased mental effort or listening effort (Pichora-Fuller et al. 2016). Over time, or in difficult listening conditions, listening effort can cause stress and mental fatigue, contributing to negative psychosocial consequences (e.g., social withdrawal) or limited/discontinued hearing-aid use (Eckert, et al., 2016; Pichora-Fuller, 2007). Additionally, the amount of listening effort required to recognize speech varies by individual and by listening condition (Pichora-Fuller, Kramer, Eckert, et al., 2016). Therefore, having a way to measure and account for listening effort in individual hearing aid fittings and aural rehabilitation plans may improve satisfaction and eventual hearing aid retention in those with hearing loss. Few objective measures are available to reliably predict listening effort in real world environments and many effort-related measures do not consider the specific neural systems that underlie listening effort (Zekveld et al., 2010; Smith et al. 2016; McMahon et al. 2016). The purpose of this study is to evaluate an electroencephalogram (EEG)-based method for quantifying listening effort based on the power of the cortical EEG response. Spectral power estimates within different EEG frequency domains that represent the activity of attention-related neural systems were calculated and included: (1) low-frequency alpha (8-10 Hz; LFA) power that has been associated with increased working memory task demands (Klimesch, 1999); (2) high-frequency alpha (10-13 Hz; HFA) power that has been associated with semantic memory and cognitive demands (Klimesch, 1999); and (3) theta (4-7 Hz) power that has been associated with encoding information (Klimesch, 1999) and increased listening effort (Wisniewski et al., 2015). The EEG data was collected during administration of the Words-In-Noise test (WIN; Wilson et al., 2003) and the Word Auditory Recognition and Recall Measure (WARRM; Smith et al., 2016) that induce listening effort due to low signal-to-noise ratio and due to auditory working memory demand, respectively. The results of correlations among EEG power in the three frequency ranges, WIN performance, WAARM performance, and self-report measures of listening effort will be presented. These results will be supported by independent component source analysis of EEG frequencies for regions of interest predicted to contribute to listening effort, including the frontal midline, auditory cortex, and parietal lobe. The EEG measures are expected to collectively explain task performance and self-reported listening effort.
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Tolaszová, Eva. "Analýza EEG signálů při Stroopově testu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218220.
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Master’s thesis deals with the measurement of biological signals for the effect of psychological burden. To monitor this effect was elected Stroop test, which is in the psychology used to detect disorders of attention and concentration. EEG and ECG signals during Stroop test were obtained using the EEG recording systém, in the context of research evoked potentials. As a part of the work it has been designed custom application for analyzing and interpreting data and statistical analysis by t-test.
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Schlitzer, Peter. "Lachgasfreie Niedrigflussnarkosen mit Desfluran und Remifentanil ein Vergleich zwischen EEG-gestützter und nicht EEG-gestützter Narkoseüberwachung /." [S.l.] : [s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972332642.
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Adamczyk, Marek [Verfasser], and Rainer [Akademischer Betreuer] Landgraf. "Genetics of human sleep EEG : analysis of EEG microstructure in twins / Marek Adamczyk. Betreuer: Rainer Landgraf." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2015. http://d-nb.info/1098130588/34.
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Tirsch, Werner. "Biomedizinische Relevanz der quantitativen EEG-Analyse." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-77176.
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He, Chen. "Person authentication using EEG brainwave signals." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/22475.
Full textAbstract:
Studies have shown that the electroencephalogram (EEC) recordings have unique pattern for each individual and thus have potential for biometric applications. There are two major problems associated with EEC biometrics. One is that the large EEG features size and the relatively limited EEG data size make it difficult to train a robust model; the other is that the signals from EEG scalp may not be reliable in many situations. Thus in this thesis we proposed new methods for increasing the accuracy and robustness of
EEC-based authentication systems.
First, to address the concern of the high dimensionality of EEC features, we proposed a novel dimension reduction method of EEG features based on the Fast Johnson-Lindeustrauss Transform (FJLT). We showed that this method has potential of mapping EEG features from a high dimension space
to a lower one while keeping discrimination power between the features of subjects. The features we used are Multivariate Autoregressive (mAR)
coefficients. We tested this method on a motor task related EEG data set. Second, to increase the reliability of scalp EEC signals, we employed
an Independent Component Analysis (ICA)-based approach in our authentication procedure, with the assumption that EEG recordings are linear
combinations of the underlying brain source signals. We estimated the Independent Components (ICs) from several physical regions on the scalp and determine the Dominating Independent Components (DIC) in the corresponding regions. Then we extracted the Univariate Autoregressive (AR)coefficients from DICs as features. We tested our algorithm on two data sets, a motor task related EEC data set and an EEC data set of P300
potential. The proposed algorithm appeared to be promising, and when applied to EEG data collected from different days yields better performance than other methods. This relative consistence over time is essential in person authentication systems.
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Laurent, François. "Predicting epileptic seizures from intracranial EEG." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32354.
Full textAbstract:
The cause of seizures in epileptic patients is still poorly understood. Ongoing debates regarding the existence of a pre-seizure state initiating the seizure remain unresolved. Most of the work on this topic has focused on the identification of forerunners (prior to the seizure occurring) in the electroencephalogram, by using measures intended to isolate and distinguish recognizable patterns. New signal processing tools have been developed to allow for a more accurate characterization of the electroencephalogram, and therefore increase the potential to detect forerunners. This study presents both a statistical and an algorithmic evaluation of the predictive value of these measures. The evaluation was carried out on limited electroencephalogram segments of five temporal epilepsy patients whose EEG was recorded at 2000 Hz. The statistical analysis suggested several pathophysiological factors influencing the seizure prediction, and the algorithm implementation succeeded in detecting 71% of pre-seizure states at a mean time of 20.9 +/- 17.4 min prior to the seizure.Les causes des crises d'épilepsie sont encore très mal comprises. L'existence d'un état préparatoire d'avant-crise occasionne de nombreux débats qui restent sans issue. La plupart des travaux sur ce sujet se concentrent sur la recherche de signes avant-coureur (avant la crise d'épilepsie) dans l'électroencéphalogramme. De nouveaux outils de traitement du signal permettent une meilleure description de l'électroencéphalogramme et ont, par conséquent, un plus fort potentiel pour la détection de signe avant-coureurs. Cette étude présente une évaluation statistique et algorithmique de ces mesures. L'évaluation se base sur un nombre de segments limité d'électroencéphalogramme échantillonné à 2000 Hz provenant de cinq patients avec épilepsie temporal. L'évaluation statistique a suggéré plusieurs facteurs pathophysiologique influençant la prédiction de crise d'épilepsie et l'algorithme a réussit à détecter 71% des états d'avant-crise à un temps moyen de 20.9 +/- 17.4 min avant la crise.
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Tsironis, P. "A shape descriptor for EEG analysis." Thesis, University of Sussex, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.374476.
Full textAbstract:
The Shape Descriptor is a tool for shape analysis of EEG data. It combines shape analysis of wave forms with medical knowledge of EEG features (spikes. slow waves and artifacts). Its contribution is that it optimizes human recognition by providing an accurate shape representation (slopes. durations. amplitudes) using mathematical criteria (error norm. randomness of error ete.) and offering valuable information about their structural properties. The Shape Descriptor has been implemented on a Unix system using Pascal language. The description of the EEG data by linear segments is achieved in two stages. Module 1 provides an initial segmentation of the wave form. The original data is approximated by a polynomial of low degree called Uniform Approximation. using as criterion of clO8el1ess the mioimn error norm. The extraction of linear segments is achieved through the use of hed error approximation techniques. These allow the description of data by straight line segments whose pointwise error does not exceed a pre-assigned value (ie the minimn error obtained in the uniform approximation). The function of Module 2 is to obtiJo better ftptesentation of the EEG data by minimizing the error norm. This is achieved by the split-and-merge algorithm which attempts to minimize the error by moving the junction points of the linear segments. Successive segments with similar approximating c:oefIicients are merged while linear segments with great error are split provided that these processes do not yield greater error. The Shape Descriptor is a good candidate for EEG shape analysis. not only for transients but alao for artifacts and moft complicated patterns
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Jayaraman, Vinoth, Sivakumaran Sivalingam, and Sangeetha Munian. "Analysis of Real Time EEG Signals." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34164.
Full textAbstract:
The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic system. Electroencephalography (EEG) technology is an efficient way of recording electrical activity of the brain. The advancement of EEG technology in biomedical application helps in diagnosing various brain disorders as tumors, seizures, Alzheimer’s disease, epilepsy and other malfunctions in human brain. The main objective of our thesis deals with acquiring and pre-processing of real time EEG signals using a single dry electrode placed on the forehead. The raw EEG signals are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored in the computer. Various machine learning techniques are preferred to classify EEG signals precisely. Different algorithms are built for analysing various signal processing techniques to process the signals. These results can be further used for the development of better Brain-computer interface systems.
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Mejía, Alberto. "Relatoría: Pensamiento Estratégico (EEG), sesión 6." Universidad Peruana de Ciencias Aplicadas - UPC, 2007. http://hdl.handle.net/10757/274414.
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Schwartzman, David J. "The EEG correlates of romantic love." Honors in the Major Thesis, University of Central Florida, 2003. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/331.
Full textAbstract:
This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.Bachelors
Arts and Sciences
Psychology
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Haji, Samadi Mohammad Reza. "Eye tracking with EEG life-style." Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/6862/.
Full textAbstract:
Innovative human-computer interaction paradigms with minimum motor control provide realistic interactions and have potential for use in assistive technologies. Among the human modalities, the eyes and the brain are the two modalities with minimum motor requirements. Most of the existing assistive technologies based on tracking the eyes (such as electrooculography and videooculography) are intrusive, limited to the laboratory environment and restrictive or are not accurate enough for real-life applications. The same limitations apply to brain activity monitoring systems such as electroencephalography (EEG). In this research, the objective is to employ a less-intrusive, consumer-grade EEG headset designed for mobile applications to track the user’s eyes and reliably estimate focus of foveal attention (FoA). To this end, signal processing approaches are proposed in order to classify different types of eye movements and estimate FoA. The FoA estimation is then improved using the brain responses to flickering stimuli recorded in EEG data. Afterwards, the FoA estimation is again improved by proposing an automated method to remove eye-related artefacts from brain responses to the stimuli. Finally, the FoA estimation is best improved by extracting eye-movement classification and brain-response detection features from EEG data projected into independent sources.
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Rollings, David T. "EEG-fMRI in epilepsy and sleep." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7287/.
Full textAbstract:
This thesis used simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) to investigate both epilepsy and sleep. Initially, EEG-fMRI was used in a cohort of patients with complex epilepsy referred from a tertiary epilepsy clinic for both pre-surgical evaluation and diagnostic reasons. The results suggest a limited utility of EEG-fMRI in the epilepsy clinic with a very complex patient group. Following on, investigation of early blood oxygen level dependent (BOLD) signal changes in a group of patients with focal epilepsy demonstrated potentially meaningful BOLD changes occurring six seconds prior to interictal epileptiform discharges, and modelling less than this six seconds can result in overlap of the haemodynamic response function used to model BOLD changes. The same analysis was used to model endogenously occurring sleep paroxysms; K-complexes (KCs), vertex sharp waves (VSWs) and sleep spindles (SSs), finding early BOLD signal changes with SSs in group data. Finally, KCs and VSWs were investigated in more detail in a group of participants under both sleep deprived and non-deprived conditions, demonstrating an increase in overall activation for both KCs and VSWs following sleep deprivation. Overall, we find early BOLD changes are not restricted to pathological events and sleep deprivation can enhance BOLD responses.
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Blum, Joshua (Joshua M. ). "Pinky : interactively analyzing large EEG datasets." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105939.
Full textAbstract:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 75-77).
In this thesis, I describe a system I designed and implemented for interactively analyzing large electroencephalogram (EEG) datasets. Trained experts, known as encephalographers, analyze EEG data to determine if a patient has experienced an epileptic seizure. Since EEG analysis is time intensive for large datasets, there is a growing corpus of unanalyzed EEG data. Fast analysis is essential for building a set of example data of EEG results, allowing doctors to quickly classify the behavior of future EEG scans. My system aims to reduce the cost of analysis by providing near real-time interaction with the datasets. The system has three optimized layers handling the storage, computation, and visualization of the data. I evaluate the design choices for each layer and compare three dierent implementations across dierent workloads.
by Joshua Blum.
M. Eng.
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Stewart, Andrew David. "Assessing EEG neuroimaging with machine learning." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20471.
Full textAbstract:
Neuroimaging techniques can give novel insights into the nature of human cognition. We do not wish only to label patterns of activity as potentially associated with a cognitive process, but also to probe this in detail, so as to better examine how it may inform mechanistic theories of cognition. A possible approach towards this goal is to extend EEG 'brain-computer interface' (BCI) tools - where motor movement intent is classified from brain activity - to also investigate visual cognition experiments. We hypothesised that, building on BCI techniques, information from visual object tasks could be classified from EEG data. This could allow novel experimental designs to probe visual information processing in the brain. This can be tested and falsified by application of machine learning algorithms to EEG data from a visual experiment, and quantified by scoring the accuracy at which trials can be correctly classified. Further, we hypothesise that ICA can be used for source-separation of EEG data to produce putative activity patterns associated with visual process mechanisms. Detailed profiling of these ICA sources could be informative to the nature of visual cognition in a way that is not accessible through other means. While ICA has been used previously in removing 'noise' from EEG data, profiling the relation of common ICA sources to cognitive processing appears less well explored. This can be tested and falsified by using ICA sources as training data for the machine learning, and quantified by scoring the accuracy at which trials can be correctly classified using this data, while also comparing this with the equivalent EEG data. We find that machine learning techniques can classify the presence or absence of visual stimuli at 85% accuracy (0.65 AUC) using a single optimised channel of EEG data, and this improves to 87% (0.7 AUC) using data from an equivalent single ICA source. We identify data from this ICA source at time period around 75-125 ms post-stimuli presentation as greatly more informative in decoding the trial label. The most informative ICA source is located in the central occipital region and typically has prominent 10-12Hz synchrony and a -5 μV ERP dip at around 100ms. This appears to be the best predictor of trial identity in our experiment. With these findings, we then explore further experimental designs to investigate ongoing visual attention and perception, attempting online classification of vision using these techniques and IC sources. We discuss how these relate to standard EEG landmarks such as the N170 and P300, and compare their use. With this thesis, we explore this methodology of quantifying EEG neuroimaging data with machine learning separation and classification and discuss how this can be used to investigate visual cognition. We hope the greater information from EEG analyses with predictive power of each ICA source quantified by machine learning separation and classification and discuss how this can be used to investigate visual cognition. We hope the greater information from EEG analyses with predictive power of each ICA source quantified by machine learning might give insight and constraints for macro level models of visual cognition.
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Salma, Nabila. "EEG Signal Analysis in Decision Making." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984237/.
Full textAbstract:
Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal processing of brain signals and how brain activity can be quantifiable through data analysis. An experiment is analyzed in this thesis to provide evidence that theta frequency band activity is associated with stress and stress is negatively correlated with concentration and problem solving, therefore hindering decision making skill. From the results of the experiment, it is seen that theta is negatively correlated to delta and beta frequency band activity, thus establishing the fact that stress affects internal focus while carrying out a task.