| Titre : |
Features extraction based on Schrödinger operator's spectrum for cognitive states classification |
| Type de document : |
texte imprimé |
| Auteurs : |
Mohamed Maoui, Auteur ; Taous Meriem Laleg Kirati, Directeur de thèse ; Chérif Larbes, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2018 |
| Importance : |
62 f. |
| Présentation : |
ill. |
| Format : |
30 cm. |
| Accompagnement : |
1 CD-ROM. |
| Note générale : |
Mémoire de Projet de Fin d’Étude : Électronique : Alger, École Nationale Polytechnique : 2018
Bibliogr. f. 61 - 62 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Lassifiers Features Cognitive states |
| Index. décimale : |
PN00518 |
| Résumé : |
Training machine learning algorithms to classify cognitive states is a challenge that many biomedical researchers are dealing with nowadays, for the numerous medical advantages that this kind of research has in understanding many neurodegenerative diseases. However, it is important to feed these classifiers with high-quality features allowing us to obtain high classification performance of cognitive states. We propose in this work, a new signal analysis modality to extract features from some specific brain regions whose activations are triggered by two mental states, performed by different subjects. We explore the efficiency of the technique and its fundamental aspects. |
Features extraction based on Schrödinger operator's spectrum for cognitive states classification [texte imprimé] / Mohamed Maoui, Auteur ; Taous Meriem Laleg Kirati, Directeur de thèse ; Chérif Larbes, Directeur de thèse . - [S.l.] : [s.n.], 2018 . - 62 f. : ill. ; 30 cm. + 1 CD-ROM. Mémoire de Projet de Fin d’Étude : Électronique : Alger, École Nationale Polytechnique : 2018
Bibliogr. f. 61 - 62 Langues : Anglais ( eng)
| Mots-clés : |
Lassifiers Features Cognitive states |
| Index. décimale : |
PN00518 |
| Résumé : |
Training machine learning algorithms to classify cognitive states is a challenge that many biomedical researchers are dealing with nowadays, for the numerous medical advantages that this kind of research has in understanding many neurodegenerative diseases. However, it is important to feed these classifiers with high-quality features allowing us to obtain high classification performance of cognitive states. We propose in this work, a new signal analysis modality to extract features from some specific brain regions whose activations are triggered by two mental states, performed by different subjects. We explore the efficiency of the technique and its fundamental aspects. |
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