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Dissertations / Theses on the topic 'Eeg'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

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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2

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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3

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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<p>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 movem
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4

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
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5

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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6

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 th
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7

Lovelace, Joseph A. "Ambulatory EEG Platform." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479816584544204.

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8

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 disc
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9

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
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10

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 variatio
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11

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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12

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 desc
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13

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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14

Frohne, Ursula. "EEG-Grundaktivität und Intelligenz." Diss., lmu, 2002. http://nbn-resolving.de/urn:nbn:de:bvb:19-7435.

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15

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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16

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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17

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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18

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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19

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 vytv
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20

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 perso
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21

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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22

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 stati
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23

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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24

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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25

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 épileptiq
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26

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 me
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27

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/.

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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
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28

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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29

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.

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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 new
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30

Chen, Tsai Yuan. "Network Electrophysiology Sensor-On-A- Chip." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-dissertations/389.

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" 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 bro
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31

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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32

Szafir, Daniel J. "Non-Invasive BCI through EEG." Thesis, Boston College, 2010. http://hdl.handle.net/2345/1208.

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Thesis advisor: Robert Signorile<br>It 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 s
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33

Ascolani, Gianluca. "EEG, Alpha Waves and Coherence." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc28389/.

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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 co
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34

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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35

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.

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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). T
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36

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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37

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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38

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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39

Pinte, Caroline. "Machine learning for bi-modal EEG-fMRI neurofeedback : EEG electrodes localization and fMRI NF scores prediction." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS053.

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Cette thèse explore l'apport des méthodes d'apprentissage automatique dans le contexte de la bi-modalité EEG-IRMf, avec pour objectif de localiser automatiquement et précisément les électrodes EEG dans un volume IRM et de prédire des scores neurofeedback IRMf à partir de signaux EEG. La première partie présente le contexte et les outils utilisés, en abordant les modalités EEG et IRMf ainsi que leur combinaison, le neurofeedback, les réseaux de neurones artificiels, la segmentation d'images et la régression de séries temporelles. La deuxième partie comprend trois contributions principales. La p
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40

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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41

He, Chen. "Person authentication using EEG brainwave signals." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/22475.

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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 dimens
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42

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.

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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 electroenceph
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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.

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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
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44

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.

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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 syste
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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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46

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.

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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.<br>Bachelors<br>Arts and Sciences<br>Psychology
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47

Haji, Samadi Mohammad Reza. "Eye tracking with EEG life-style." Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/6862/.

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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 electroencephalo
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48

Rollings, David T. "EEG-fMRI in epilepsy and sleep." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7287/.

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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 c
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49

Blum, Joshua (Joshua M. ). "Pinky : interactively analyzing large EEG datasets." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105939.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 75-77).<br>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
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50

Stewart, Andrew David. "Assessing EEG neuroimaging with machine learning." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20471.

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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
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