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Relevant bibliographies by topics / Electroencephalography (EEG) signal / Dissertations / Theses

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Dissertations / Theses on the topic 'Electroencephalography (EEG) signal'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Birch, Gary Edward. "Single trial EEG signal analysis using outlier information." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28626.

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The goal of this thesis work was to study the characteristics of the EEG signal and then, based on the insights gained from these studies, pursue an initial investigation into a processing method that would extract useful event related information from single trial EEG. The fundamental tool used to study the EEG signal characteristics was autoregressive modeling. Early investigations pointed to the need to employ robust techniques in both model parameter estimation and signal estimation applications. Pursuing robust techniques ultimately led to the development of a single trial processing meth

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2

Sellergren, Albin, Tobias Andersson, and Jonathan Toft. "Signal processing through electroencephalography : Independent project in electrical engineering." Thesis, Uppsala universitet, Elektricitetslära, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298771.

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This report is about a project where electroencephalography (EEG) wasused to control a two player game. The signals from the EEG-electrodeswere amplified, filtered and processed. Then the signals from the playerswere compared and an algorithm decided what would happen in the gamedepending on which signal was largest. The controls and the gaming mechanismworked as intended, however it was not possible to gather a signal fromthe brain with the method used in this project. So ultimately the goal wasnot reached.<br>electroencephalography, EEG

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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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4

Salma, Nabila. "EEG Signal Analysis in Decision Making." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984237/.

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Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal proc

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Hodulíková, Tereza. "Analýza EEG během anestezie." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220369.

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This master's thesis deals with the method of functional examination of brain electric activity. In the first part is description of central nervous system, method of electroencephalography and possible connections. Furthermor the project involves characteristic of EEG signal and its artifacts. It also includes signal processing and list of symptoms, which will be used for an analysis of the EEG during anesthesia. The second part of thesis involves development of application, which allow viewing and proccesing of EEG signal. In conclusion of thesis is carried out unequal segmentation and stati

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Mulyana, Ridwan S. "A Low Voltage, Low Power 4th Order Continuous-time Butterworth Filter for Electroencephalography Signal Recognition." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281981810.

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7

Esteller, Rosana. "Detection of seizure onset in epileptic patients from intracranial EEG signals." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15620.

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8

Shahriari, Sheyda. "Electroencephalography (EEG) profile and sense of body ownership : a study of signal processing, proprioception and tactile illusion." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16299.

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With the ability to feel through artificial limbs, users regain more function and increasingly see the prosthetics as parts of their own bodies. So, main focus of this project was dedicated to recuperating sensation by deception both in sighted and unsighted patients, started with illusionary experiments on healthy volunteers, brain signals were captured with medical EEG headsets during these tests to have a better understanding of how the brain works during body ownership illusions. EEG results suggest that gender difference exists in the perception of body transfer illusion. Visual input can

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9

Bendoukha, Hocine. "Détection automatique des évènements paroxystiques dans le signal EEG." Rouen, 1989. http://www.theses.fr/1989ROUES029.

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Etude des méthodes de détection automatiques des évènements paroxystiques électroencéphalographiques chez des malades épileptiques. Les signaux EEG proviennent d'enregistrements ambulatoires de longue durée. L'approche consiste à définir des descripteurs quantitatifs et qualitatifs du signal EEG

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10

Courtellemont, Pierre. "Architecture multi-processeurs pour le traitement du signal EEG." Rouen, 1989. http://www.theses.fr/1989ROUES003.

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L'algorithme proposé repose sur le principe des moindres carrés récursifs. Il diffère des méthodes usuelles par une adaptation des paramètres qui se fait globalement sur une fenêtre d'observation et non à chaque nouvel échantillon. Cette technique a permis la mise au point d'un algorithme de détection à deux seuils successifs

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11

Castillo, Ober, Simy Sotomayor, Guillermo Kemper, and Vincent Clement. "Correspondence Between TOVA Test Results and Characteristics of EEG Signals Acquired Through the Muse Sensor in Positions AF7–AF8." Smart Innovation, Systems and Technologies, 2021. http://hdl.handle.net/10757/653803.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>This paper seeks to study the correspondence between the results of the test of variable of attention (TOVA) and the signals acquired by the Muse electroencephalogram (EEG) in the positions AF7 and AF8 of the cerebral cortex. There are a variety of research papers that estimates an index of attention in which the different characteristics in discrete signals of the brain activity were used. However, many of these results were obtained without c

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12

Ablin, Pierre. "Exploration of multivariate EEG /MEG signals using non-stationary models." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT051.

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L'Analyse en Composantes Indépendantes (ACI) modèle un ensemble de signaux comme une combinaison linéaire de sources indépendantes. Cette méthode joue un rôle clé dans le traitement des signaux de magnétoencéphalographie (MEG) et électroencéphalographie (EEG). L'ACI de tels signaux permet d'isoler des sources de cerveau intéressantes, de les localiser, et de les séparer d'artefacts. L'ACI fait partie de la boite à outils de nombreux neuroscientifiques, et est utilisée dans de nombreux articles de recherche en neurosciences. Cependant, les algorithmes d'ACI les plus utilisés ont été développés

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13

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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Rousseau, Sandra. "Influence du retour sensoriel dans les interfaces cerveau machine EEG : étude du potentiel d'erreur." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENT101/document.

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Dans cette thèse nous proposons d'étudier le potentiel d'erreur et sa possible intégration dans les ICMs (Interfaces cerveau machine). Le potentiel d'erreur (ErrP) est un potentiel généré par le cerveau lors de l'observation d'une erreur. Sa détection essai par essai pourrait permettre la mise en place d'une boucle de contrôle dans les ICMs. Cependant son RSB étant très faible cette détection est difficile. Ici nous proposons une étude complète de ce système. Dans un premier temps nous étudions de manière détaillée ses différentes caractéristiques (temporelles, fréquentielles..). A partir de c

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Samandari, Rohan. "Integration of Bluetooth Sensors in a Windows-Based Research Platform." Thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43037.

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This thesis describes how to build a solution for transmitting data from an Electroencephalography (EEG) device to a server in real-time while guiding the user through a number of predefined exercises. This solution will be used by Spinal Cord Injury (SCI) patients suffering from neuropathic pain, in order to understand if it is possible to predict such pain from EEG. The collected data will help clinicians analyze the brain activity data from patients who can submit the data from their home. To accomplish this development task, an application was built that connects to a portable EE

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Kalunga, Emmanuel. "Vers des interfaces cérébrales adaptées aux utilisateurs : interaction robuste et apprentissage statistique basé sur la géométrie riemannienne." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV041/document.

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Au cours des deux dernières décennies, l'intérêt porté aux interfaces cérébrales ou Brain Computer Interfaces (BCI) s’est considérablement accru, avec un nombre croissant de laboratoires de recherche travaillant sur le sujet. Depuis le projet Brain Computer Interface, où la BCI a été présentée à des fins de réadaptation et d'assistance, l'utilisation de la BCI a été étendue à d'autres applications telles que le neurofeedback et l’industrie du jeux vidéo. Ce progrès a été réalisé grâce à une meilleure compréhension de l'électroencéphalographie (EEG), une amélioration des systèmes d’enregistreme

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Renfrew, Mark E. "A Comparison of Signal Processing and Classification Methods for Brain-Computer Interface." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1246474708.

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18

Gallego, Jutglà Esteve. "New signal processing and machine learning methods for EEG data analysis of patients with Alzheimer's disease." Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2015. http://hdl.handle.net/10803/290853.

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Les malalties neurodegeneratives són un conjunt de malalties que afecten al cervell. Aquestes malalties estan relacionades amb la pèrdua progressiva de l'estructura o la funció de les neurones, incloent-hi la mort d'aquestes. La malaltia de l'Alzheimer és una de les malalties neurodegeneratives més comunes. Actualment, no es coneix cap cura per a l'Alzheimer, però es creu que hi ha un grup de medicaments que el que fan és retardar-ne els principals símptomes. Aquests s'han de prendre en les primeres fases de la malaltia ja que sinó no tenen efecte. Per tant, el diagnòstic precoç de la malalti

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Polanský, Štěpán. "Zpracování elektroencefalografických signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219242.

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This work describes basics of electroencaphalography, measuring electroencaphalography signals, their processing and evaluation. There is discussed method of topography mapping of brain activity called brainmapping. The practical part contains description of design aplication in Matlab.

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Grosselin, Fanny. "Apprentissage neuronal par neurofeedback à l’aide d’un système EEG portable : application à la réduction du stress chez l'Homme." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS125.pdf.

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Cette thèse porte sur la conception, l’implémentation et l’évaluation d’un système de neurofeedback EEG portable, d’aide à la gestion du stress, à destination du grand public. Un tel système permet aux utilisateurs d’apprendre à moduler leurs états mentaux par des phénomènes de plasticité cérébrale. Cependant, plusieurs facteurs peuvent compliquer cet apprentissage, comme un plus faible rapport signal sur bruit de l'EEG acquis par des électrodes sèches, la contamination par des artefacts ou encore la définition de paramètres pertinents à partir des signaux EEG. Afin d’optimiser ce retour neuro

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Semeráková, Nikola. "Detekce bdělosti mozku ze skalpového EEG záznamu za pomoci vyšších statistických metod." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378032.

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Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using thes

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Acar, Erman. "Classification Of Motor Imagery Tasks In Eeg Signal And Its Application To A Brain-computer Interface For Controlling Assistive Environmental Devices." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612994/index.pdf.

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This study focuses on realization of a Brain Computer Interface (BCI)for the paralyzed to control assistive environmental devices. For this purpose, different motor imagery tasks are classified using different signal processing methods. Specifically, band-pass filtering, Laplacian filtering, and common average reference (CAR) filtering areused to enhance the EEG signal. For feature extraction<br>Common Spatial Pattern (CSP), Power Spectral Density (PSD), and Principal Component Analysis (PCA) are tested. Linear Feature Normalization (LFN), Gaussian Feature Normalization (GFN), and Unit-norm F

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23

Hitziger, Sebastian. "Modélisation de la variabilité de l'activité électrique dans le cerveau." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4015/document.

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Cette thèse explore l'analyse de l'activité électrique du cerveau. Un défi important de ces signaux est leur grande variabilité à travers différents essais et/ou différents sujets. Nous proposons une nouvelle méthode appelée "adaptive waveform learning" (AWL). Cette méthode est suffisamment générale pour permettre la prise en compte de la variabilité empiriquement rencontrée dans les signaux neuroélectriques, mais peut être spécialisée afin de prévenir l'overfitting du bruit. La première partie de ce travail donne une introduction sur l'électrophysiologie du cerveau, présente les modalités d'e

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Banville, Hubert. "Enabling real-world EEG applications with deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG005.

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Au cours des dernières décennies, les avancées révolutionnaires en neuroimagerie ont permis de considérablement améliorer notre compréhension du cerveau. Aujourd'hui, avec la disponibilité croissante des dispositifs personnels de neuroimagerie portables, tels que l'EEG mobile " à bas prix ", une nouvelle ère s’annonce où cette technologie n'est plus limitée aux laboratoires de recherche ou aux contextes cliniques. Les applications de l’EEG dans le " monde réel " présentent cependant leur lot de défis, de la rareté des données étiquetées à la qualité imprévisible des signaux et leur résolution

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Samadi, Samareh. "EEG-fMRI integration for identification of active brain regions using sparse source decomposition." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT021/document.

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L'électroencéphalographie (EEG) est une technique d'imagerie cérébrale non invasive importante, capable d'enregistrer l'activité neuronale avec une grande résolution temporelle (ms), mais avec une résolution spatiale faible. Le problème inverse en EEG est un problème difficile, fortement sous-déterminé : des contraintes ou des a priori sont nécessaires pour aboutir à une solution unique. Récemment, l'intégration de signaux EEG et d'imagerie par résonance magnétique fonctionnelle (fMRI) a été largement considérée. Les données EEG et fMRI relatives à une tâche donnée, reflètent les activités neu

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Massias, Mathurin. "Sparse high dimensional regression in the presence of colored heteroscedastic noise : application to M/EEG source imaging." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT053.

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Parmi les techniques d’imagerie cerébrale, la magneto- et l’électro-encéphalographie se distinguent pour leur faible degré d’invasivité et leur excellente résolution temporelle. La reconstruction de l’activité neuronale à partir de l’enregistrement des champs électriques et magnétiques constitue un problème inverse extr êmement mal posé, auquel il est nécessaire d’ajouter des contraintes pour le résoudre. Une approche populaire, empruntée dans ce manuscrit, est de postuler que la solution est parcimonieuse spatialement, ce qui peut s’obtenir par une pénalisation L2/1. Cependant, ce type de rég

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Lahr, Jacob [Verfasser], and Andreas [Akademischer Betreuer] Schulze-Bonhage. "Electromyographic signals in intracranial electroencephalographic recordings = Elektromyographische Signale in intrakraniellen EEG-Aufnahmen." Freiburg : Universität, 2012. http://d-nb.info/1123473927/34.

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Bhalotiya, Anuj Arun. "Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984122/.

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In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them t

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SAIBENE, AURORA. "A Flexible Pipeline for Electroencephalographic Signal Processing and Management." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/360550.

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L'elettroencefalogramma (EEG) fornisce registrazioni non-invasive delle attività e delle funzioni cerebrali sotto forma di serie temporali, a loro volta caratterizzate da una risoluzione temporale e spaziale (dipendente dai sensori), e da bande di frequenza specifiche per alcuni tipi di condizioni cerebrali. Tuttavia, i segnali EEG risultanti sono non-stazionari, cambiano nel tempo e sono eterogenei, essendo prodotti da differenti soggetti e venendo influenzati da specifici paradigmi sperimentali, condizioni ambientali e dispositivi. Inoltre, questi segnali sono facilmente soggetti a rumore e

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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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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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Martínez, Cristina G. B. "Nonlinear signal analysis of micro and macro electroencephalographic recordings from epilepsy patients." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670397.

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The use of nonlinear signal analysis measures to characterize electroencephalographic (EEG) recordings can be key for a better understanding of the underlying brain dynamics. In neurological disorders such as epilepsy, these dynamics are altered as result of a disturbed coordination between neuronal populations. The aim of this thesis is to characterize the seizure-free interval of EEG recordings from epilepsy patients by means of nonlinear signal analysis techniques to investigate whether this type of analysis can contribute to the localization of the seizure onset zone, the brain region from

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Nagabushan, Naresh. "Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90183.

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Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG

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Burger, Christiaan. "A novel method of improving EEG signals for BCI classification." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95984.

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Thesis (MEng)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Muscular dystrophy, spinal cord injury, or amyotrophic lateral sclerosis (ALS) are injuries and disorders that disrupts the neuromuscular channels of the human body thus prohibiting the brain from controlling the body. Brain computer interface (BCI) allows individuals to bypass the neuromuscular channels and interact with the environment using the brain. The system relies on the user manipulating his neural activity in order to control an external device. Electroencephalography (EEG) is a cheap, non-invasive, real time ac

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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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Ясунова, Масума Пулатівна. "Метод оцінки інтегральної активності ЕЕГ під впливом аудіо сигналів". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/43674.

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Обсяг звіту становить 57 сторінок, міститься 32 ілюстрації, 14 таблиць, 5 формул, 2 додатки. Загалом опрацьовано 36 джерел. Актуальність даної роботи полягаєу визначенні залежності біоелектричної активності головного мозку від амплітудно-частотних характеристик звукового сигналу. У наш час музика супроводжує наше життя, тому важливо визначити який вплив вона має на електричну активність головного мозку і як саме змінюються характеристики показників мозкової активності при прослуховуванні музичних сигналів. Мета:визначити ефективність впливу аудіо сигналів різного амплітудно-частотного скла

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Del, Castello Mariangela. "Analysis of electroencephalography signals collected in a magnetic resonance environment: characterisation of the ballistocardiographic artefact." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13214/.

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L’acquisizione simultanea di segnali elettroencefalografici (EEG) e immagini di risonanza magnetica funzionale (fMRI) permette di investigare attivazioni cerebrali in modo non invasivo. La presenza del campo magnetico altera però in modo non trascurabile la qualità dei segnali EEG acquisiti. In particolare due artefatti sono stati individuati: l’artefatto da gradiente e l’artefatto da ballistocardiogramma (BCG). L’artefatto da BCG è legato all’attività cardiaca del soggetto, ed è caratterizzato da elevata variabilità tra un’occorrenza e l’altra in termini di ampiezza, forma d’onda e durata del

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Ramantani, Georgia. "Contribution des sources épileptiques inter-critiques et critiques à l’EEG de scalp." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0034/document.

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Plusieurs études de simulation in vitro et in vivo ont été réalisées au cours des dernières décennies afin de clarifier les interrelations des sources corticales avec leurs corrélats électrophysiologiques enregistrés sur l’EEG invasif et l’EEG de scalp. L’amplitude des potentiels corticaux, l’étendue de l’aire corticale impliquée par la décharge, de même que la localisation et la géométrie de la source corticale sont des facteurs indépendants qui modulent l’observabilité et la contribution de ces sources sur l’EEG de surface. L’enregistrement simultané et multi-échelle de l’EEG de scalp et int

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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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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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Somon, Bertille. "Corrélats neuro-fonctionnels du phénomène de sortie de boucle : impacts sur le monitoring des performances." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAS042/document.

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Les mutations technologiques à l’œuvre dans les systèmes aéronautiques ont profondément modifié les interactions entre l’homme et la machine. Au fil de cette évolution, les opérateurs se sont retrouvés face à des systèmes de plus en plus complexes, de plus en plus automatisés et de plus en plus opaques. De nombreuses tragédies montrent à quel point la supervision des systèmes par des opérateurs humains reste un problème sensible. En particulier, de nombreuses évidences montrent que l’automatisation a eu tendance à éloigner l’opérateur de la boucle de contrôle des systèmes, créant un phénom

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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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Abazid, Majd. "Topological study of the brain functional organization at the early stages of Alzheimer's disease using electroencephalography." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS026.

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L'électroencéphalographie (EEG) est encore considérée de nos jours comme une technique de neuroimagerie très utile dans les applications cliniques, adaptée aux patients souffrant de troubles cognitifs et physiques, ainsi qu'aux tests à grande échelle. L'EEG est une technologie non invasive, peu coûteuse et facilement accessible. Elle se caractérise par une haute résolution temporelle, ce qui est crucial pour le suivi de la dynamique cérébrale.Plusieurs travaux dans la littérature ont exploité l'EEG pour étudier les altérations de l'activité cérébrale liées aux maladies neurodégénératives, nota

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Vélez, Luis, and Guillermo Kemper. "Algorithm for Detection of Raising Eyebrows and Jaw Clenching Artifacts in EEG Signals Using Neurosky Mindwave Headset." Smart Innovation, Systems and Technologies, 2021. http://hdl.handle.net/10757/653818.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>The present work proposes an algorithm to detect and identify the artifact signals produced by the concrete gestural actions of jaw clench and eyebrows raising in the electroencephalography (EEG) signal. Artifacts are signals that manifest in the EEG signal but do not come from the brain but from other sources such as flickering, electrical noise, muscle movements, breathing, and heartbeat. The proposed algorithm makes use of concepts and knowl

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Lotte, Fabien. "Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications." Phd thesis, INSA de Rennes, 2008. http://tel.archives-ouvertes.fr/tel-00356346.

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Une Interface Cerveau-Ordinateur (ICO) est un système de communication qui permet à ses utilisateurs d'envoyer des commandes à un ordinateur via leur activité cérébrale, cette activité étant mesurée, généralement par ÉlectroEncéphaloGraphie (EEG), et traitée par le système. Dans la première partie de cette thèse, dédiée au traitement et à la classification des signaux EEG, nous avons cherché à concevoir des ICOs interprétables et plus efficaces. Pour ce faire, nous avons tout d'abord proposé FuRIA, un algorithme d'extraction de caractéris- tiques utilisant les solutions inverses. Nous avons ég

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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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Dvořák, Jiří. "Biofeedback a jeho použití." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217977.

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The aim of this work is describe common methods of biological feedback therapy that is used to treat some psychosomatic diseases. Subsequently, the description is focused on minimal brain dysfunction treatment by the help of EEG biofeedback. Properties and technical requirements for this therapy are concretized. The last part of this thesis is dedicated to the design and realization of practical software tool for EEG biofeedback therapy which is made in LabView 7.1. The M535 acquisition unit and NI USB-6221 measuring device are used for hardware solution.

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MISHRA, BHAVESH. "ANALYSIS AND CLASSIFICATION OF EEG SIGNALS USING MACHINE LEARNING ALGORITHMS." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20036.

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Electroencephalography (EEG) data analysis and categorization are critical for recognising brain activity and diagnosis different neurological illnesses. This research describes a unique approach for analysing and classifying EEG data, as well as its possible uses for health care and brain-computer interface, or BCI, applications. To extract relevant characteristics from EEG data, the proposed method employs modern signal processing methods and machine learning algorithms. These properties record both of them temporal and spectral aspects of brain activity, allowing for good differe

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Searle, Andrew. "Electrode performance and signal processing strategies for the discrimination of EEG alpha waves: implications for environmental control by unconstrained subjects without training." 2000. http://hdl.handle.net/2100/855.

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The phenomenon of the increase in alpha EEG activity associated with eye closure has been shown to be successful for implementing environmental control for disabled persons. Studies in this thesis investigate strategies which improve the reliability, robustness, and ease of use of alpha EEG control systems. Primarily, research covers the effectiveness of alpha EEG detection algorithms (with regard to detection time and susceptibility to artifact) and the construction and use of EEG sensing electrodes. Many new techniques for the detection of the increase of alpha EEG associated with eye closur

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Searle, AP. "Electrode performance and signal processing strategies for the discrimination of EEG alpha waves : implications for environmental control by unconstrained subjects without training." Thesis, 2000. http://hdl.handle.net/10453/19996.

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University of Technology, Sydney. Faculty of Science.<br>The phenomenon of the increase in alpha EEG activity associated with eye closure has been shown to be successful for implementing environmental control for disabled persons. Studies in this thesis investigate strategies which improve the reliability, robustness, and ease of use of alpha EEG control systems. Primarily, research covers the effectiveness of alpha EEG detection algorithms (with regard to detection time and susceptibility to artifact) and the construction and use of EEG sensing electrodes. Many new techniques for the detect

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