| Titre : |
Mobile robot control via brain computer interface and fatigue detection based on EEG signals |
| Type de document : |
document électronique |
| Auteurs : |
Younes Moussaoui, Auteur ; Mahdi Latreche, Auteur ; Mohamed Tadjine, Directeur de thèse ; Messaoud Chakir, Directeur de thèse ; Guiatni, Mohamed, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2021 |
| Importance : |
132 f., 1 fichier PDF (10 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2021
Bibliogr. f. 125-132 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
EEG
BCI
Motor imagery
Fatigue
Feature extraction
Machine learning
Deep
Learning
Genetic algorithm |
| Index. décimale : |
PA02221 |
| Résumé : |
In the last decade, the rapid development of complex methods for recording brain signals and the exponential rise of available computing power as well as the increased awareness of brain dysfunctions and mental disorders, have led researchers to use large-scale neurophysiological recordings for abnormal behaviours detection, diseases diagnosis, and motor control. Electroencephalograms (EEG) are a very popular measurement for brain activities because of their non-invasive nature and their wide spectrum of possible applications. In this context, two applications have been developed in this project, the first aims to design a novel Brain Computer Interface (BCI) architecture based on Motor Imagery (MI) for real time control of a mobile robot. Spectral power
computing, multi-class Common Spatial Pattern (CSP), and Machine Learning (ML) have been used to reach this aim. The second involves the proposal of an approach for fatigue detection using machine Learning (ML), Deep Learning (DL), and Genetic Algorithms (GA). |
Mobile robot control via brain computer interface and fatigue detection based on EEG signals [document électronique] / Younes Moussaoui, Auteur ; Mahdi Latreche, Auteur ; Mohamed Tadjine, Directeur de thèse ; Messaoud Chakir, Directeur de thèse ; Guiatni, Mohamed, Directeur de thèse . - [S.l.] : [s.n.], 2021 . - 132 f., 1 fichier PDF (10 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2021
Bibliogr. f. 125-132 Langues : Anglais ( eng)
| Mots-clés : |
EEG
BCI
Motor imagery
Fatigue
Feature extraction
Machine learning
Deep
Learning
Genetic algorithm |
| Index. décimale : |
PA02221 |
| Résumé : |
In the last decade, the rapid development of complex methods for recording brain signals and the exponential rise of available computing power as well as the increased awareness of brain dysfunctions and mental disorders, have led researchers to use large-scale neurophysiological recordings for abnormal behaviours detection, diseases diagnosis, and motor control. Electroencephalograms (EEG) are a very popular measurement for brain activities because of their non-invasive nature and their wide spectrum of possible applications. In this context, two applications have been developed in this project, the first aims to design a novel Brain Computer Interface (BCI) architecture based on Motor Imagery (MI) for real time control of a mobile robot. Spectral power
computing, multi-class Common Spatial Pattern (CSP), and Machine Learning (ML) have been used to reach this aim. The second involves the proposal of an approach for fatigue detection using machine Learning (ML), Deep Learning (DL), and Genetic Algorithms (GA). |
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