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Relevant bibliographies by topics / EEG signal pattern / Dissertations / Theses

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Dissertations / Theses on the topic 'EEG signal pattern'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Giovanini, Renato de Macedo [UNESP]. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/151710.

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Submitted by Renato de Macedo Giovanini null (renato81243@aluno.feis.unesp.br) on 2017-09-25T14:52:54Z No. of bitstreams: 1 dissertacao_renato_de_macedo_giovanini_2017_final.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5)<br>Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-09-27T20:24:55Z (GMT) No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5)<br>Made available in DSpace on 2017-09-27T20:24:55Z (GMT). No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7

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Giovanini, Renato de Macedo. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications /." Ilha Solteira, 2017. http://hdl.handle.net/11449/151710.

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Orientador: Aparecido Augusto de Carvalho<br>Resumo: There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world co

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Kortelainen, J. (Jukka). "EEG-based depth of anesthesia measurement:separating the effects of propofol and remifentanil." Doctoral thesis, Oulun yliopisto, 2011. http://urn.fi/urn:isbn:9789514294853.

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Abstract Within the last few decades, electroencephalogram (EEG) has become a widely used tool for the automatic assessment of depth of anesthesia. The EEG-based depth of anesthesia measurement has been associated with several advantages, such as a decreased incidence of intraoperative awareness and recall, faster recovery, and reduced consumption of anesthetics. However, the measurement is challenged by simultaneous administration of different types of anesthetics, which is the common practice in the operating rooms today. Especially, the assessment of depth of anesthesia induced by supplemen

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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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Perera, Biyagama. "Identification of EEG signal patterns between adults with dyslexia and normal controls." Thesis, Perera, Biyagama (2017) Identification of EEG signal patterns between adults with dyslexia and normal controls. PhD thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/39939/.

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Electroencephalography (EEG) is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, it plays a vital role in detecting neurological disorders such as epilepsy. Dyslexia is a hidden learning disability with a neurological origin affecting a significant amount of the world population. Studies show unique brain structures and behaviours in individuals with dyslexia and these variations have become more evident with the use of techniques such as EEG, Functional Magnetic Resonanc

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6

Derner, Marlene [Verfasser]. "Pattern Recognition in Intracranial EEG Signals and Prediction of Memory / Marlene Derner." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1229989153/34.

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Vezard, Laurent. "Réduction de dimension en apprentissage supervisé. Application à l'étude de l'activité cérébrale." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00926845.

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L'objectif de ce travail est de développer une méthode capable de déterminer automatiquement l'état de vigilance chez l'humain. Les applications envisageables sont multiples. Une telle méthode permettrait par exemple de détecter automatiquement toute modification de l'état de vigilance chez des personnes qui doivent rester dans un état de vigilance élevée (par exemple, les pilotes ou les personnels médicaux). Dans ce travail, les signaux électroencéphalographiques (EEG) de 58 sujets dans deux états de vigilance distincts (état de vigilance haut et bas) ont été recueillis à l'aide d'un casque à

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Dornhege, Guido. "Increasing information transfer rates for brain-computer interfacing." Phd thesis, [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=98051276X.

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Tobón, Cardona M. (Marcela). "Automatic detection of early repolarization pattern in ECG signals with waveform prototype-based learning." Master's thesis, University of Oulu, 2018. http://jultika.oulu.fi/Record/nbnfioulu-201809062750.

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Abstract. Early repolarization (ER) pattern was considered a benign finding until 2008, when it was associated with sudden cardiac arrest (SCA). Since then, the interest of the medical community on the topic has grown, stating the need to develop methods to detect the pattern and analyze the risk of SCA. This thesis presents an automatic detection method of ER using supervised classification. The novelty of the method lies in the features used to construct the classification models. The features consist of prototypes that are composed by fragments of the ECG signal where the ER pattern is loca

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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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Giacobbe, Martina. "Analysis of transient states in EMG signals with applications to robotic prostheses." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15861/.

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The purpose of this thesis is to analyze and classify EMG signals for prosthetic control. For transradial amputees the electrical signal of muscles is acquired, processed and classified with the final scope of moving a prosthetic hand. This signal has a first transient state related to the dynamic part of the movement starting from rest and a subsequent steady state which corresponds to the sustained and voluntary contraction. Up to now the classification has been performed only on the steady state, instead in this thesis we will include also the transient. The classification of the transient,

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Altamirano, Altamirano Alvaro. "EMG Pattern Prediction for Upper Limb Movements Based on Wavelet and Hilbert-Huang Transform." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0261/document.

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Cette thèse porte sur l’analyse des signaux sEMG multicanaux à l’aide de la transformée en ondelettes, de la transformation de Hilbert-Huang et d’autres méthodes d’analyse ou de traitement des signaux, telles que le filtrage de Kalman et de Goertzel, pour détecter, mesurer, filtrer et décomposer les signaux sEMG afin d’identifier des tendances dans le temps et en fréquence des mouvements de flexion-extension des doigts de la main en observant les signaux myoélectriques des muscles superficiels. Le but est de prédire le mouvement des doigts de la main et de minimiser le temps de calcul pour per

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Baccherini, Simona. "Pattern recognition methods for EMG prosthetic control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12033/.

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In this work we focus on pattern recognition methods related to EMG upper-limb prosthetic control. After giving a detailed review of the most widely used classification methods, we propose a new classification approach. It comes as a result of comparison in the Fourier analysis between able-bodied and trans-radial amputee subjects. We thus suggest a different classification method which considers each surface electrodes contribute separately, together with five time domain features, obtaining an average classification accuracy equals to 75% on a sample of trans-radial amputees. We propose an a

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14

Altamirano, Altamirano Alvaro. "EMG Pattern Prediction for Upper Limb Movements Based on Wavelet and Hilbert-Huang Transform." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0261.

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Cette thèse porte sur l’analyse des signaux sEMG multicanaux à l’aide de la transformée en ondelettes, de la transformation de Hilbert-Huang et d’autres méthodes d’analyse ou de traitement des signaux, telles que le filtrage de Kalman et de Goertzel, pour détecter, mesurer, filtrer et décomposer les signaux sEMG afin d’identifier des tendances dans le temps et en fréquence des mouvements de flexion-extension des doigts de la main en observant les signaux myoélectriques des muscles superficiels. Le but est de prédire le mouvement des doigts de la main et de minimiser le temps de calcul pour per

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15

Manzan, William Alexandre. "Utilização das transformadas Wavelets na extração de características e no reconhecimento de padrão em um sinal de ECG." Universidade Federal de Uberlândia, 2006. https://repositorio.ufu.br/handle/123456789/12553.

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Presently doctors and medical professionals are having the aid of support systems for diagnosis which include in their data base expertise knowledge, acquired mainly through empirical observation of heuristics applied to their diagnosis which translate partially, to the technical application used. In the systems which aid diagnosis, more specifically in electrocardiograms, the methods of pattern recognition simulate the skills of the cardiologist, when, from a set of characteristics extracted from the signal, it is possible to infer a determined heart condition, indicated by some cardiopathy.

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Zanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.

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Hand gesture recognition based on surface electromyographic (sEMG) signals is a promising approach for the development of Human-Machine Interfaces (HMIs) with a natural control, such as intuitive robot interfaces or poly-articulated prostheses. However, real-world applications are limited by reliability problems due to motion artifacts, postural and temporal variability, and sensor re-positioning. This master thesis is the first application of deep learning on the Unibo-INAIL dataset, the first public sEMG dataset exploring the variability between subjects, sessions and arm postures, by colle

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17

Shokouh, Aghaei Amirhossein. "Separable Spatio-spectral Patterns in EEG signals During Motor-imagery Tasks." Thesis, 2014. http://hdl.handle.net/1807/65750.

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Brain-Computer Interface (BCI) systems aim to provide a non-muscular channel for the brain to control external devices using electrical activities of the brain. These BCI systems can be used in various applications, such as controlling a wheelchair, neuroprosthesis, or speech synthesizer for disabled individuals, navigation in virtual environment, and assisting healthy individuals in performing highly demanding tasks. Motor-imagery BCI systems in particular are based on decoding imagination of motor tasks, e.g., to control the movement of a wheelchair or a mouse curser on the computer screen a

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18

Qian, Ming. "Fusion Methods for Detecting Neural and Pupil Responses to Task-relevant Visual Stimuli Using Computer Pattern Analysis." Diss., 2008. http://hdl.handle.net/10161/669.

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<p>A series of fusion techniques are developed and applied to EEG and pupillary recording analysis in a rapid serial visual presentation (RSVP) based image triage task, in order to improve the accuracy of capturing single-trial neural/pupillary signatures (patterns) associated with visual target detection.</p><p>The brain response to visual stimuli is not a localized pulse, instead it reflects time-evolving neurophysiological activities distributed selectively in the brain. To capture the evolving spatio-temporal pattern, we divide an extended (``global") EEG data epoch, time-locked to each im

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19

Supriya, Supriya. "Brain Signal Analysis and Classification by Developing New Complex Network Techniques." Thesis, 2020. https://vuir.vu.edu.au/40551/.

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Brain signal analysis has a crucial role in the investigation of the neuronal activity for diagnosis of brain diseases and disorders. The electroencephalogram (EEG) is the most efficient biomarker for the analysis of brain signal that assists in the diagnosis of brain disorder medication and also plays an essential role in all the neurosurgery related to the brain. EEG findings illustrate the meticulous condition, and clinical content of the brain dysfunctions, and has an undisputed importance role in the detection of epilepsy condition and sleep disorders and dysfunctions allied to alcohol. T

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20

Wu, Feng-Jen, and 吳鳳甄. "Pattern Recognition for ECG Signal Analysis." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/72521099851716263871.

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碩士<br>國立清華大學<br>資訊系統與應用研究所<br>97<br>Heart disease is the third of the top ten causes of deaths in Taiwan these years. The heart disease cause of the cardiovascular disease is unexpected hence developing a real-time heart disease detector system is important. In this thesis we implement an ECG signal classification system with these stages: signal pre-processing, wavelet transform, and signal classification. In the first stage, median filter is used to reduce the noise and baseline wander on the ECG signal. Then we detect the QRS complex and extract the features of QRS complex by wavelet transf

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21

Wei, Jyh-Jong, and 魏志中. "SVD Based Pattern Analysis for Quasiperiodic Signal Processing -- ECG Data Compression and Noise Reduction." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/04476274982877606132.

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博士<br>國立臺灣大學<br>電機工程學研究所<br>90<br>The goal of this dissertation is to propose a pattern analysis method using singular value decomposition (SVD) for quasiperiodic signal processing. The SVD based technique is employed as a robust tool for the ECG data compression and noise reduction. The approach of the pattern analysis is exploited as an innovative method to decompose ECG waveforms as a linear combination of a set of essential patterns and the associated weighting vectors. Thanks to the specific feature of cyclic nature and high inter-beat correlations of ECG cycles, SVD especial

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22

Andrews, ALEXANDER. "Finger Movement Classification Using Forearm EMG Signals." Thesis, 2008. http://hdl.handle.net/1974/1574.

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To a person with an upper limb amputation or congenital defect, a well-functioning prosthesis can open the door to many work and life opportunities. A fundamental component of many modern prostheses is the myoelectric control system, which uses the myoelectric signals from an individual's muscles to control prosthetic movements. Though much research has been done involving the myoelectric control of arm and gross hand movements, more dexterous finger control has not received the same attention. Consequently, the goal of this study was to determine an optimal approach to the myoelectric sign

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He, Kun. "Automated Measurement of Neuromuscular Jitter Based on EMG Signal Decomposition." Thesis, 2007. http://hdl.handle.net/10012/3332.

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The quantitative analysis of decomposed electromyographic (EMG) signals reveals information for diagnosing and characterizing neuromuscular disorders. Neuromuscular jitter is an important measure that reflects the stability of the operation of a neuromuscular junction. It is conventionally measured using single fiber electromyographic (SFEMG) techniques. SFEMG techniques require substantial physician dexterity and subject cooperation. Furthermore, SFEMG needles are expensive, and their re-use increases the risk of possible transmission of infectious agents. Using disposable concentric needle (

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24

Parsaei, Hossein. "EMG Signal Decomposition Using Motor Unit Potential Train Validity." Thesis, 2011. http://hdl.handle.net/10012/6299.

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Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are ei

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Σαψάνης, Χρήστος. "Αναγνώριση βασικών κινήσεων του χεριού με χρήση ηλεκτρομυογραφήματος". Thesis, 2013. http://hdl.handle.net/10889/6420.

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Ο στόχος αυτής της εργασίας ήταν η αναγνώριση έξι βασικών κινήσεων του χεριού με χρήση δύο συστημάτων. Όντας θέμα διεπιστημονικού επιπέδου έγινε μελέτη της ανατομίας των μυών του πήχη, των βιοσημάτων, της μεθόδου της ηλεκτρομυογραφίας (ΗΜΓ) και μεθόδων αναγνώρισης προτύπων. Παράλληλα, το σήμα περιείχε αρκετό θόρυβο και έπρεπε να αναλυθεί, με χρήση του EMD, να εξαχθούν χαρακτηριστικά αλλά και να μειωθεί η διαστασιμότητά τους, με χρήση των RELIEF και PCA, για βελτίωση του ποσοστού επιτυχίας ταξινόμησης. Στο πρώτο μέρος γίνεται χρήση συστήματος ΗΜΓ της Delsys αρχικά σε ένα άτομο και στη συνέχεια

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