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Relevant bibliographies by topics / EEG, electroencephalogram / Dissertations / Theses

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Dissertations / Theses on the topic 'EEG, electroencephalogram'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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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2

Duta, Mihaela D. "The study of vigilance using neural networks analysis of EEG." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301454.

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3

Lee, Pamela Wen-Hsin. "Mutual information derived functional connectivity of the electroencephalogram (EEG)." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/219.

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Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with

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4

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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5

Riddington, Edward Peter. "Automated interpretation of the background EEG using fuzzy logic." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1109.

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A new framework is described for managing uncertainty and for dealing with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective

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D'ROZARIO, Angela Louise. "Electroencephalogram (EEG) biomarkers of neurobehavioural dysfunction in obstructive sleep apnea." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/9886.

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Obstructive sleep apnea (OSA) affects an estimated 2-4% of middle–aged adults yet we are still exploring how best to delineate the neurophysiological deficits that accompany this disorder. Untreated OSA leads to an increased risk of motor vehicle accidents. Traditional polysomnographic (PSG) metrics do not consistently correlate with daytime functioning. There is a clinical need for simple biomarkers to identify individuals susceptible to OSA-related cognitive deficits. There is a close relationship between EEG-based changes in brain activity and daytime function in healthy sleepers. No studi

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Löfhede, Johan. "Classification of Burst and Suppression in the Neonatal EEG." Licentiate thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3448.

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The brain requires a continuous supply of oxygen and even a short period ofreduced oxygen supply risks severe and lifelong consequences for theaffected individual. The delivery is a vulnerable period for a baby who mayexperience for example hypoxia (lack of oxygen) that can damage the brain.Babies who experience problems are placed in an intensive care unit wheretheir vital signs are monitored, but there is no reliable way to monitor thebrain directly. Monitoring the brain would provide valuable informationabout the processes going on in it and could influence the treatment and helpto improve

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Murrell, Joanna. "Spontaneous EEG changes in the equine surgical patient." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340352.

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9

Tcheslavski, Gleb V. "Coherence and Phase Synchrony Analysis of Electroencephalogram." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/30186.

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Phase Synchrony (PS) and coherence analyses of stochastic time series - tools to discover brain tissue pathways traveled by electrical signals - are considered for the specific purpose of processing of the electroencephalogram (EEG). We propose the Phase Synchrony Processor (PSP), as a tool for implementing phase synchrony analysis, and examine its properties on the basis of known signals. Long observation times and wide filter bandwidths can decrease bias in PS estimates. The value of PS is affected by the difference in frequency of the sequences being analyzed and can be related to that freq

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10

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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Holt, Mark Rowan Gorton. "The use of neural networks in the analysis of the anaesthetic electroencephalogram." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390525.

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12

Roessgen, Mark Andrew. "Analysis and modelling of EEG data with application to seizure detection in the newborn." Thesis, Queensland University of Technology, 1996. https://eprints.qut.edu.au/105543/1/T%28BE%26E%29%201033%20Analysis%20and%20modelling%20of%20EEG%20data%20with%20application%20to%20seizure%20detection%20in%20the%20newborn.pdf.

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The exact relationship between the electroencephalogram (EEG) measured at the scalp, and the internal dynamical organisation of the brain that generated it, is still very much an open question. This has led to difficulties in EEG analysis and interpretation, which in turn has relegated the EEG largely to the role of a corroborative, rather than stand alone diagnostic tool. This thesis investigates the use of signal processing techniques for more accurate quantification of the EEG. It is anticipated that through improved quantification, a better diagnostic value for the EEG will result.

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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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Rodriguez, Ricardo J. "An Electroencephalogram (EEG) Based Biometrics Investigation for Authentication| A Human-Computer Interaction (HCI) Approach." Thesis, Nova Southeastern University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3723216.

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<p> Encephalogram (EEG) devices are one of the active research areas in human-computer interaction (HCI). They provide a unique brain-machine interface (BMI) for interacting with a growing number of applications. EEG devices interface with computational systems, including traditional desktop computers and more recently mobile devices. These computational systems can be targeted by malicious users. There is clearly an opportunity to leverage EEG capabilities for increasing the efficiency of access control mechanisms, which are the first line of defense in any computational system. </p><p> A

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Zamora, Mayela E. "The study of the sleep and vigilance electroencephalogram using neural network methods." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365699.

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Wu, Shuai. "Mot robust cross-subject klassificering av electroencephalogram (EEG) baserad brain-computer interfacing (BCI):En genomförbarhetsstudie." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254765.

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Brain-computer interface(BCI) är ett system där man kan skicka kommandon till dator med bara hjärnaktivitet. En sådan system är viktigt för människor lider av flera motorisk funktionshinder, då maskinen skulle kunna förbättra patienters liv genom att uppfylla deras behov. Denna rapport fokusera på en variant av BCI, kallas motor imagery based BCI, vilken basera på att klassificera försökspersons hjärnaktivitet då han/hon tänka sig att röra sin kroppsdelar. Det finns flera svårighet för att bygga en fungerande system, en av de är generalisering av tränad model. En tränad model garanti inte exak

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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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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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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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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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Crossen, Samantha Lokelani. "Investigation of Variability in Cognitive State Assessment based on Electroencephalogram-derived Features." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316025164.

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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

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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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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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Montgomery, Mason. "OPTIMIZATION OF FEATURE SELECTION IN A BRAIN-COMPUTER INTERFACE SWITCH BASED ON EVENT-RELATED DESYNCHRONIZATION AND SYNCHRONIZATION DETECTED BY EEG." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2786.

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There are hundreds of thousands of people who could benefit from a Brain-Computer Interface. However, not all are willing to undergo surgery, so an EEG is the prime candidate for use as a BCI. The features of Event-Related Desynchronization and Synchronization could be used for a switch and have been in the past. A new method of feature selection was proposed to optimize classification of active motor movement vs a non-active idle state. The previous method had pre-selected which frequency and electrode to use as electrode C3 at the 20Hz bin. The new method used SPSS statistical software

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Segalotto, Matheus. "ARNI: an EEG-Based Model to Measure Program Comprehension." Universidade do Vale do Rio dos Sinos, 2018. http://www.repositorio.jesuita.org.br/handle/UNISINOS/7019.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-24T13:44:05Z No. of bitstreams: 1 Matheus Segalotto_.pdf: 8717126 bytes, checksum: 94fda4721d448e49b82be91aaa8057c7 (MD5)<br>Made available in DSpace on 2018-04-24T13:44:05Z (GMT). No. of bitstreams: 1 Matheus Segalotto_.pdf: 8717126 bytes, checksum: 94fda4721d448e49b82be91aaa8057c7 (MD5) Previous issue date: 2018-01-18<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>PROSUP - Programa de Suporte à Pós-Gradução de Instituições de Ensino Particulares<br>A compreensão de programa é um processo cognitivo

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Estepp, Justin Ronald. "An improved adaptive filtering approach for removing artifact from the electroencephalogram." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1433244703.

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HERATH, MUTHUKUMARA MUDIYANSELAGE Samantha Chandani. "Using EEG measures to quantify reduced daytime vigilance in patients diagnosed with obstructive sleep apnoea using a novel electroencephalogram analysis method." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9726.

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Introduction Vigilance in obstructive sleep apnoea (OSA) does not correlate well with disease severity/ symptoms: Hence the need for a simple objective test. One such method could be quantitative analysis of the awake electroencephalogram (qEEG). qEEG is conventionally analysed using Power Spectral Analysis (PSA) looking at different EEG frequencies of delta, theta, alpha and beta. A novel method of analyzing the qEEG: De-trended fluctuation analysis (DFA) provides a single value: the scaling exponent (SE), which measures the fluctuations in the EEG signal. Artefact removal from qEEG is mand

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Mahadevan, Anandi. "Real Time Ballistocardiogram Artifact Removal in EEG-fMRI Using Dilated Discrete Hermite Transform." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1226235813.

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Loughran, Sarah Patricia, and n/a. "The efffects of eletromagnetic fields emitted by mobile phones on human sleep and melatonin production." Swinburne University of Technology, 2007. http://adt.lib.swin.edu.au./public/adt-VSWT20070731.100218.

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The use of mobile phones is continually increasing throughout the world, with recent figures showing that there are currently more than 2 billion mobile phone users worldwide. However, despite the recognised benefits of the introduction and widespread use of mobile phone technologies, concerns regarding the potential health effects of exposure to the radiofrequency electromagnetic fields emitted by mobile phone handsets have similarly increased, leading to an increase in demand for scientific research to investigate the possibility of health effects related to the use of mobile phones. An incr

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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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CHIKUSHI, Rohgi Toshio Meneses. "Análise não linear de sinais de EEG : uma aplicação de redes complexas." Universidade Federal Rural de Pernambuco, 2014. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/6720.

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Submitted by (edna.saturno@ufrpe.br) on 2017-03-30T14:56:43Z No. of bitstreams: 1 RohgiToshio Meneses Chikushi.pdf: 6493487 bytes, checksum: b95c0c692d050783c78c20f7a212f0e6 (MD5)<br>Made available in DSpace on 2017-03-30T14:56:43Z (GMT). No. of bitstreams: 1 RohgiToshio Meneses Chikushi.pdf: 6493487 bytes, checksum: b95c0c692d050783c78c20f7a212f0e6 (MD5) Previous issue date: 2014-08-29<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES<br>The electroencephalogram (EEG) is still an important tool in the diagnosis of neurodiseases. As recording technique offers an excell

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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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Price, Gregory Walter. "Application of time series analysis techniques to the human electroencephalogram in real time, in order to synchronise event related potentials (ERPS) with background EEG." Thesis, Queensland University of Technology, 1995.

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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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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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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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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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Jarjees, Mohammed Sabah. "The causality between Electroencephalogram (EEG) and Central Neuropathic Pain (CNP), and the effectiveness of neuromodulation strategies on cortical excitability and CNP in patients with spinal cord injury." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/7985/.

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Spinal Cord Injury has primary consequences visible immediately upon injury and secondary consequence which develop some time after injury. One of the primary consequences of SCI is loss or impairment of sensory and motor functions. Related secondary consequences of the injury are Central Neuropathic Pain (CNP) and spasticity. Several studies have found that CNP can affect the cortical activity of the patient and long term CNP causes anatomical cortical changes. Therefore, early prediction and treatment of CNP could potentially prevent these changes and hopefully increase responsiveness to the

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Haig, Albert Roland. "Missing Links the role of phase synchronous gamma oscillations in normal cognition and their dysfunction in schizophrenia." University of Sydney. Psychological Medicine, 2002. http://hdl.handle.net/2123/848.

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SUMMARY Introduction: There has recently been a great deal of interest in the role of synchronous high-frequency gamma oscillations in brain function. This interest has been motivated by an increasing body of evidence, that oscillations which are synchronous in phase across separated neuronal populations, may represent an important mechanism by which the brain binds or integrates spatially distributed processing activity which is related to the same object. Many models of schizophrenia suggest an impairment in the integration of brain processing, such as a loosening of associations, disc

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Jaworska, Natalia. "Electrophysiological Indices in Major Depressive Disorder and their Utility in Predicting Response Outcome to Single and Dual Antidepressant Pharmacotherapies." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22873.

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Certain electrophysiological markers hold promise in distinguishing individuals with major depressive disorder (MDD) and in predicting antidepressant response, thereby assisting with assessment and optimizing treatment, respectively. This thesis examined resting brain activity via electroencephalographic (EEG) recordings, as well as EEG-derived event-related potentials (ERPs) to auditory stimuli and facial expression presentations in individuals with MDD and controls. Additionally, the utility of resting EEG as well as auditory ERPs (AEPs), and the associated loudness-dependence of AEPs (LDAEP

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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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Wang, Anli. "Functional significance of human sensory ERPs : insights from modulation by preceding events." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:2dcd4959-8638-4ee1-b591-3eb28bdf3a1d.

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The electroencephalogram (EEG) reflects summated, slow post-synaptic potentials of cortical neurons. Sensory, motor or cognitive events (such as a fast-rising sensory stimulus, a brisk self-paced movement or a stimulus-triggered cognitive task) can elicit transient changes in the ongoing human EEG, called event-related potentials (ERPs). ERPs are widely used in clinical practice, and believed to reflect the activity of the sensory system activated by the stimulus (for example, laser-evoked potentials are used to substantiate the neuropathic nature of clinical pain conditions). When ERPs are el

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Ta, Christopher Ian. "Interfacing a Brain Control Interface towards the Development of a Retrofitted, Low-Cost, Open Sourced, Electric Wheelchair." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1707240/.

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The Emotiv Insight is a commercially available, low-cost, mobile EEG device that is commonly overshadowed by its costlier counterpart, the Emotiv EPOC. The purpose of this report is to investigate if the Emotiv Insight is a suitable headset to be used as a controlling factor in conjunction with an Arduino microcontroller and various electrical components that are used towards the development of an open-sourced, affordable electric wheelchair with the primary goal of providing those who either do not have the financial resources or the physical capability to operate a traditional wheelchair due

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Azami, Hamed. "Entropy-based nonlinear analysis for electrophysiological recordings of brain activity in Alzheimer's disease." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31106.

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Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells causes memory loss and cognitive decline. As AD progresses, changes in the electrophysiological brain activity take place. Such changes can be recorded by the electroencephalography (EEG) and magnetoencephalography (MEG) techniques. These are the only two neurophysiologic approaches able to directly measure the activity of the brain cortex. Since EEGs and MEGs are considered as the outputs of a nonlinear system (i.e., brain), there has been an interest in nonlinear methods for the analysis of EEGs and ME

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Gomes, Rodrigo San Martin Ignacio. "Avaliação do filtro sensório-motor através de registro de eletroencefalograma (EEG) e teste de inibição pré-pulso (IPP) em pacientes após primeiro episódio psicótico." reponame:Repositório Institucional da UFABC, 2017.

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Orientadora: Profa. Dra. Cristiane Otero Reis Salum<br>Coorientador: Prof. Dr. Francisco José Fraga da Silva<br>Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, São Bernardo do Campo, 2017.<br>Pacientes de transtorno bipolar e esquizofrenia apresentam déficits no processamento de informação. Dentre esses déficits está uma disfunção do mecanismo de filtragem sensorial, que pode ser observada através do teste de Inibição Pré-Pulso (IPP), que acessa a inibição das respostas muscular, observada por eletromiografia (EMG) e neural, observada

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De, Rivière Najat. "Apprentissage graphomoteur du système graphique arabe chez les adultes français : étude comparative des données EEG brutes et de l'effet de types de modèles de présentation sur les paramètres de l'écriture." Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1269.

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La recherche des dernières décennies en neurosciences, nous a fourni une compréhension plus détaillée sur le cerveau, grâce aux techniques de neuro-imagerie, qui permettent l'étude de l'activité cérébrale en temps réel. Les neuroscientifique considèrent le cerveau comme l'organe responsable de l'apprentissage (Vinter & Chartrel, 2009 ; Belghit, 2021; Campbell & Pagé, 2012). Ce dernier peut avoir lieu grâce aux réactions cerveau/stimulus (Gaussel & Reverdy, 2013), au cours desquelles, le cerveau perçoit, traite et intègre l'information portée par le stimulus (Gaussel et Reverdy, 201

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