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Relevant bibliographies by topics / Electroencephalogram signal / Dissertations / Theses

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Dissertations / Theses on the topic 'Electroencephalogram signal'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Fatoorechi, Mohsen. "Electroencephalogram signal acquisition in unshielded noisy environment." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/55034/.

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Researchers have used electroencephalography (EEG) as a window into the activities of the brain. High temporal resolution coupled with relatively low cost compares favourably to other neuroimaging techniques such as magnetoencephalography (MEG). For many years silver metal electrodes have been used for non-invasive monitoring electrical activities of the brain. Although these electrodes provide a reliable method for recording EEG they suffer from noise, such as offset potentials and drifts, and usability issues, e.g. skin prepa- ration and short circuiting of adjacent electrodes due to gel run

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2

Winski, R. "Adaptive techniques for signal enhancement in the human electroencephalogram." Thesis, Keele University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372829.

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3

Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.

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Mylonas, Socrates Andreou. "Signal modelling : a versatile approach for the automatic analysis of the electroencephalogram." Thesis, City University London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283270.

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5

Nussbaum, Paul. "Signal Processing of Electroencephalogram for the Detection of Attentiveness towards Short Training Videos." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/558.

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This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of att

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Kwong, Siu-shing. "Detection of determinism of nonlinear time series with application to epileptic electroencephalogram analysis." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35512222.

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7

Young, Andrew Coady. "A Consensus Model for Electroencephalogram Data Via the S-Transform." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etd/1424.

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A consensus model combines statistical methods with signal processing to create a better picture of the family of related signals. In this thesis, we will consider 32 signals produced by a single electroencephalogram (EEG) recording session. The consensus model will be produced by using the S-Transform of the individual signals and then normalized to unit energy. A bootstrapping process is used to produce a consensus spectrum. This leads to the consensus model via the inverse S-Transform of the consensus spectrum. The method will be applied to both a control and experimental EEG to show how th

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8

Kawaguchi, Hirokazu. "Signal Extraction and Noise Removal Methods for Multichannel Electroencephalographic Data." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/188593.

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9

Alhajjar, Yasser. "Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG." Thesis, Angers, 2017. http://www.theses.fr/2017ANGE0020/document.

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L’électroencéphalogramme (EEG), mesure de l'activité électrique du cerveau, reste une des meilleures méthodes de prévision non-invasive des résultats neurologiques. L'objectif de notre travail est de développer un système de classification automatique qui prévoit des risques sur la maturation cérébrale, se traduisant par un état pathologique à 2 ans. Les caractéristiques du signal EEG, qui sont utiles à la prévision automatisée, sont traitées via un module appelée EEGDiag, et sont appliquées sur un ensemble de données issues de 397 dossiers de nouveau-nés prématurés. Chaque dossier comprend un

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Hajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.

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Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit e

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Mileros, Martin D. "A Real-Time Classification approach of a Human Brain-Computer Interface based on Movement Related Electroencephalogram." Thesis, Linköping University, Department of Mechanical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2824.

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<p>A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time. </p><p>Algorithms in the preprocessing were Blind Signal Separation a

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Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16200/1/Luke_Rankine_Thesis.pdf.

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Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible me

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Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16200/.

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Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible me

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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

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Hassanpour, Hamid. "Time-frequency based detection of newborn EEG seizure." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15853/1/Hamid_Hassanpour_Thesis.pdf.

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Neurological diseases in newborns are usually first revealed by seizures, which are characterised by a synchronous discharge of a large number of neurons. Failure to control seizures may lead to brain damage or even death. The importance of this problem prompted many researchers to look for accurate automatic methods for seizure detection. Nonstationarity and multicomponent behaviour of newborn EEG signals made this task very challenging. The significant overlap in the characteristic of background and seizure activities in newborn EEG signals added to the difficulty of seizure detection.

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Hassanpour, Hamid. "Time-Frequency Based Detection of Newborn EEG Seizure." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15853/.

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Neurological diseases in newborns are usually first revealed by seizures, which are characterised by a synchronous discharge of a large number of neurons. Failure to control seizures may lead to brain damage or even death. The importance of this problem prompted many researchers to look for accurate automatic methods for seizure detection. Nonstationarity and multicomponent behaviour of newborn EEG signals made this task very challenging. The significant overlap in the characteristic of background and seizure activities in newborn EEG signals added to the difficulty of seizure detection.

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Labounek, René. "Analýza souvislostí mezi simultánně měřenými EEG a fMRI daty." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219743.

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Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created

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Cantisani, Giorgia. "Neuro-steered music source separation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT038.

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Dans cette thèse, nous abordons le défi de l'utilisation d'interfaces cerveau-machine (ICM) sur l'application spécifique de la séparation de sources musicales qui vise à isoler les instruments individuels qui sont mélangés dans un enregistrement de musique. Ce problème a été étudié pendant des décennies, mais sans jamais considérer les ICM comme un moyen possible de guider et d'informer les systèmes de séparation. Plus précisément, nous avons étudié comment l'activité neuronale caractérisée par des signaux électroencéphalographiques (EEG) reflète des informations sur la source à laquelle on po

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Bartůšek, Jan. "Time Frequency Analysis of ERP Signals." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-412769.

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Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomo

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Rmeily, Patrick. "Reliable and efficient transmission of compressive-sensed electroencephalogram signals." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50026.

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As technologies around us are emerging at a rapid rate, wireless body sensor networks (WBSN)s are increasingly being deployed to provide comfort and safety to patients. WBSNs can monitor the patient's health and transmit the collected data to a remote location where it can be assessed. Such data is collected and transmitted using low battery devices such as specialized sensors or even smart phones. To elongate the battery life, the energy spent on acquiring, processing and transmitting the data should be minimized. The thesis addresses the case of electroencephalogram (EEG)signals. It studies

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21

Mathew, Blesy Anu. "ENTROPY OF ELECTROENCEPHALOGRAM (EEG) SIGNALS CHANGES WITH SLEEP STATE." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_theses/203.

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We hypothesized that temporal features of EEG are altered in sleep apnea subjects comparedto normal subjects. The initial aim was to develop a measure to discriminate sleep stages innormals. The longer-term goal was to apply these methods to identify differences in EEGactivity in sleep apnea subjects from normals. We analyzed the C3A2 EEG and anelectrooculogram (EOG) recorded from 9 normal adults awake and in rapid eye movement(REM) and non-REM sleep. The EEG signals were filtered to remove EOG contamination. Twomeasures of the irregularity of EEG signals, Sample Entropy (SpEn) and Tsallis Ent

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El, Sayed Hussein Jomaa Mohamad. "Signal processing of electroencephalograms with 256 sensors in epileptic children." Thesis, Angers, 2019. http://www.theses.fr/2019ANGE0028.

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Dans cette thèse, nous proposons des méthodes de traitement du signal et les appliquons à des signaux d’électro-encéphalographie (EEG) enregistrés chez des patients épileptiques. L’objectif est de pouvoir quantifier l’état du patient et d’étudier l’évolution du trouble neurologique au cours du temps. Les méthodes que nous avons développées sont basées sur des mesures d’entropie. Ainsi, nous introduisons la « multivariate Improved Weighted Multi-scale Permutation Entropy» (mvIWMPE) que nous appliquons à des signaux EEG d’enfants sains et épileptiques. Elle donne des résultats prometteurs. Nous

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Orellana, Marco Antônio Pinto. "Seizure detection in electroencephalograms using data mining and signal processing." Universidade Federal de Viçosa, 2017. http://www.locus.ufv.br/handle/123456789/11589.

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Submitted by Reginaldo Soares de Freitas (reginaldo.freitas@ufv.br) on 2017-08-22T13:26:59Z No. of bitstreams: 1 texto completo.pdf: 5760621 bytes, checksum: f90e38633fae140744262e882dc7ae5d (MD5)<br>Made available in DSpace on 2017-08-22T13:26:59Z (GMT). No. of bitstreams: 1 texto completo.pdf: 5760621 bytes, checksum: f90e38633fae140744262e882dc7ae5d (MD5) Previous issue date: 2017-03-10<br>Agencia Boliviana Espacial<br>A epilepsia é uma das doenças neurológicas mais comuns definida como a predisposição a sofrer convulsões não provocadas. A Organização Mundial da Saúde estima que 50 mi

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Radüntz, Thea. "Kontinuierliche Bewertung psychischer Beanspruchung an informationsintensiven Arbeitsplätzen auf Basis des Elektroenzephalogramms." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17417.

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Die Informations- und Kommunikationstechnologien haben die Arbeitswelt grundlegend verändert. Durch den Einsatz komplexer, hochautomatisierter Systeme werden an die kognitive Leistungsfähigkeit und Belastbarkeit von Arbeitnehmern hohe Anforderungen gestellt. Über die Ermittlung der psychischen Beanspruchung des Menschen an Arbeitsplätzen mit hohen kognitiven Anforderungen wird es möglich, eine Über- oder Unterbeanspruchung zu vermeiden. Gegenstand der Dissertation ist deshalb die Entwicklung, Implementierung und der Test eines neuen Systems zur kontinuierlichen Bewertung psychischer Beanspruch

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Vennelaganti, Swetha. "AGING AND SLEEP STAGE EFFECTS ON ENTROPY OF ELECTROENCEPHALOGRAM SIGNALS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/553.

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The aging brain is characterized by alteration in synaptic contacts, which leads to decline of motor and cognitive functions. These changes are reflected in the age related shifts in power spectrum of electroencephalogram (EEG) signals in both wakefulness and sleep. Various non-linear measures have been used to obtain more insights from EEG analysis compared to the conventional spectral analysis. In our study we used Sample Entropy to quantify regularity of the EEG signal. Because elderly subjects arouse from sleep more often than younger subjects, we hypothesized that Entropy of EEG signals f

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Milon-Harnois, Gaëlle. "Détection automatique et analyse des oscillations à haute fréquence en EEG-HD de surface." Electronic Thesis or Diss., Angers, 2023. http://www.theses.fr/2023ANGE0054.

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Un tiers des épileptiques ne voient pas d'amélioration avec les traitements actuels, poussant les médecins à envisager la chirurgie pour enlever la zone cérébrale générant les crises. Les Oscillations à Haute Fréquence (HFO) émergent comme biomarqueur pour localiser ces zones épileptogènes, mais leur détection est difficile en raison de leur rareté et de leur brièveté. En EEG de scalp la faible amplitude du signal complexifie la tâche. Cette thèse vise à automatiser la détection de HFO dans des signaux EEG-HD enregistrés à 1 KHz sur 256 électrodes chez 5 patients. Après marquage visuel des HFO

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Janwattanapong, Panuwat. "Connectivity Analysis of Electroencephalograms in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3906.

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This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the

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Fauvel, Simon. "Energy-efficient compressed sensing frameworks for the compression of electroencephalogram signals." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45359.

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The use of wireless body sensor networks (WBSNs) is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery-powered sensors is limited. In this thesis, we study the wireless transmission of electroencephalogram (EEG) signals. We propose novel, energy-efficient compressed sensing (CS) frameworks that take advantage of the inherent structure present in EEG signals (both temporal and spatial correlations) to efficiently

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Li, Chang. "Complexity Analysis of Physiological Time Series with Applications to Neonatal Sleep Electroencephalogram Signals." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1345657829.

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Rasheed, S. "RECOGNITION OF PRIMARY COLOURS IN ELECTROENCEPHALOGRAPH SIGNALS USING SUPPORT VECTOR MACHINES." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155486.

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In this study we have worked on the classification of EEG signals produced by the exposure of primary colours (RGB). The main goal of this study was to perform an offline analysis and classification of color information obtained from EEG signals recorded in response to individual RGB colours presentation in order to verify our hypothesis, if the observation of different colors can be detected or not by selecting different frequency bands. We have also performed an offline analysis of EEG signals produced by the colour imagination to observe similarities in EEG signals between actual color expo

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Kanneganti, Raghuveer. "CLASSIFICATION OF ONE-DIMENSIONAL AND TWO-DIMENSIONAL SIGNALS." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/892.

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This dissertation focuses on the classification of one-dimensional and two-dimensional signals. The one-dimensional signal classification problem involves the classification of brain signals for identifying the emotional responses of human subjects under given drug conditions. A strategy is developed to accurately classify ERPs in order to identify human emotions based on brain reactivity to emotional, neutral, and cigarette-related stimuli in smokers. A multichannel spatio-temporal model is employed to overcome the curse of dimensionality that plagues the design of parametric multivariate c

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Ma, Jiaxin. "Research on Human-Machine Interfaces of Vigilance Estimation and Robot Control based on Biomedical Signals." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199268.

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Qassim, Yahya Taher. "FPGA Design and Implementation of Wavelet Coherence for EEG Signals." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/366086.

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The EEG waveform provides millisecond resolution brain information that can be obtained from the scalp using metal electrodes. It has become an applicable measure for a wide range of brain functionalities (including higher cognition) due to its low cost, non-invasiveness and ease of access. An important EEG application uses an evoked form of these signals linked to an external stimulus. For this thesis, an EEG was acquired during presentation of an oddball task and recording the event related potential (ERP), in which the P300 component is the most important. It reflects the participant’s resp

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Janeček, David. "Sdružená EEG-fMRI analýza na základě heuristického modelu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221334.

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The master thesis deals with the joint EEG-fMRI analysis based on a heuristic model that describes the relationship between changes in blood flow in active brain areas and in the electrical activity of neurons. This work also discusses various methods of extracting of useful information from the EEG and their influence on the final result of joined analysis. There were tested averaging methods of electrodes interest, decomposition by principal components analysis and decomposition by independent component analysis. Methods of averaging and decomposition by PCA give similar results, but informa

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Yao, Bing. "ANALYSIS OF ELECTRICAL AND MAGNETIC BIO-SIGNALS ASSOCIATED WITH MOTOR PERFORMANCE AND FATIGUE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1140813534.

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McCooey, Conor Gerard, and cmccooey@ieee org. "Characterising Evoked Potential Signals using Wavelet Transform Singularity Detection." RMIT University. Electrical and Computer Engineering, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080829.101311.

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This research set out to develop a novel technique to decompose Electroencephalograph (EEG) signal into sets of constituent peaks in order to better describe the underlying nature of these signals. It began with the question; can a localised, single stimulation of sensory nervous tissue in the body be detected in the brain? Flash Visual Evoked Potential (VEP) tests were carried out on 3 participants by presenting a flash and recording the response in the occipital region of the cortex. By focussing on analysis techniques that retain a perspective across different domains ï¿½ temporal (time)

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Korczowski, Louis. "Méthodes pour l'électroencéphalographie multi-sujet et application aux interfaces cerveau-ordinateur." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT078/document.

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L'étude par neuro-imagerie de l'activité de plusieurs cerveaux en interaction (hyperscanning) permet d'étendre notre compréhension des neurosciences sociales. Nous proposons un cadre pour l'hyperscanning utilisant les interfaces cerveau-ordinateur multi-utilisateur qui inclut différents paradigmes sociaux tels que la coopération ou la compétition. Les travaux de cette thèse comportent trois contributions interdépendantes. Notre première contribution est le développement d'une plateforme expérimentale sous la forme d'un jeu vidéo multijoueur, nommé Brain Invaders 2, contrôlé par la classificati

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Koppikar, Samir Dilip. "Privacy Preserving EEG-based Authentication Using Perceptual Hashing." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955127/.

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The use of electroencephalogram (EEG), an electrophysiological monitoring method for recording the brain activity, for authentication has attracted the interest of researchers for over a decade. In addition to exhibiting qualities of biometric-based authentication, they are revocable, impossible to mimic, and resistant to coercion attacks. However, EEG signals carry a wealth of information about an individual and can reveal private information about the user. This brings significant privacy issues to EEG-based authentication systems as they have access to raw EEG signals. This thesis proposes

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Turi, Federica. "Interface cerveau-ordinateur adaptée à l'utilisateur." Thesis, Université Côte d'Azur, 2020. https://tel.archives-ouvertes.fr/tel-03149221.

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Les interfaces cerveau-ordinateur (Brain-Computer Interface ou BCI) permettent la communication entre l’utilisateur et la machine, grâce à la traduction de l’activité cérébrale en commandes qui servent à contrôler différents dispositifs. De nombreuses limitations empêchent la diffusion des systèmes BCI dans des applications réelles, telles que la phase de calibration qui résulte de la variabilité entre sessions et entre sujets. Cette phase est fondamentale car elle permet de régler les paramètres nécessaires pour le bon fonctionnement du système, mais elle est considérée beaucoup trop longue e

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Spinnato, Juliette. "Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4727/document.

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Cette thèse s’inscrit dans le contexte de l’analyse et de la classification de signaux électroencéphalogrammes (EEG) par des méthodes d’analyse discriminante. Ces signaux multi-capteurs qui sont, par nature, très fortement corrélés spatialement et temporellement sont considérés dans le plan temps-fréquence. En particulier, nous nous intéressons à des signaux de type potentiels évoqués qui sont bien représentés dans l’espace des ondelettes. Par la suite, nous considérons donc les signaux représentés par des coefficients multi-échelles et qui ont une structure matricielle électrodes × coefficien

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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/.

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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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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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Liu, Tsung-Lun, and 劉宗倫. "Electroencephalogram Signal Identification Using Artificial Neural Networks." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/24845171375133631033.

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碩士<br>中華大學<br>工業管理學系碩士班<br>102<br>With rapid developments of sensor-measurement technology in recent years, such as touch screens and voice controls replacing traditional devices like keyboards and mice, operations of human-machine interfaces (HMIs) have found themselves moving toward a mature phase. It is even anticipated that virtual vision and non-contact controls such as body heat and electroencephalogram would be the next-stage HMIs. Electroencephalogram has been a front-line source of analyzing human motions in research, however, their relationships remain fuzzy and beyond our knowledge

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Tseng, Yu-Chieh, and 曾于倢. "Electroencephalogram Signal Processing for Embedded System Applications." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/06790452128206151426.

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碩士<br>中華大學<br>電機工程學系碩士班<br>102<br>Our purpose is constructed a low-cost electroencephalograph (EEG) measurement system that can estimate high accuracy brain wave. In particular, users will not go to laboratories or hospitals. They can measure EEG signals, anytime. These EEG measurement results will provide to the professionals for more analyses and applications. Our proposed EEG recorder includes following characteristics: low cost, compact size, portability, easy to use, long record. 　　EEG analog front-end circuits of our proposition are based on operational amplifiers that are applied to con

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YANG, CHIA-HAO, and 楊家豪. "Electroencephalogram Signal Analysis when Playing On-line Games." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/32136490451722371291.

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Kavitha, V. "Chaotic Modeling Of Electroencephalographic Signals With Application To Compression." Thesis, 1998. https://etd.iisc.ac.in/handle/2005/1559.

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Kavitha, V. "Chaotic Modeling Of Electroencephalographic Signals With Application To Compression." Thesis, 1998. http://etd.iisc.ernet.in/handle/2005/1559.

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Bhat, Jyoti. "Signal processing techniques for artifact removal in electroencephalogram (EEG)." 2009. http://hdl.handle.net/10106/2047.

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Kundu, Sourav. "Analysis of Electroencephalogram Signal for P300 Based Brain-Computer Interface Speller." Thesis, 2020. http://ethesis.nitrkl.ac.in/10221/1/2020_PhD_SKundu_514EC1002_Analysis.pdf.

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Abrain-computer interface (BCI) speller is a communication medium with the outer world for the patients suffering with neuro-muscular disorders. A P300 speller which translates the brain signal into machine commands provides such communication inter-face to convey their thought without any motor movement. A P300 speller aims to spell characters by using the electroencephalogram (EEG) signal and its performance can be defined by the number of correctly recognised characters. P300 is an event-related potential (ERP) which is appeared in the EEG signal when random stimuli occur to the subject. Va

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WANG, NENG-HSUAN, and 王能軒. "Detection of Sleep Apnea using Deep Learning Algorithm based on Electroencephalogram Signal." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5nc383.

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碩士<br>輔仁大學<br>資訊工程學系碩士班<br>107<br>Sleep apnea is one type of sleep disorders. Among them, obstructive sleep apnea is the most common one. Clinically, for diagnosing obstructive sleep apnea (OSA) it usually relies on a variety of physiological signals, such as Electroencephalography (EEG), Electrocardiography and Electrooculography, for the Polysomnographic technician to perform evaluations for the diagnoses of sleep apnea. Therefore, a system for detecting sleep apnea based on EEG signal is proposed in this thesis. The system consists of two modules, a signal preprocessor and a respiratory arr

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