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Features extraction based on Schrödinger operator's spectrum for cognitive states classification / Mohamed Maoui 

Public ISBD 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 ; Larbes, Chérif, 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 ; Larbes, Chérif, 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.

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Spécialité	Etat_Exemplaire
P000279	PN00518	Papier + ressource électronique	Bibliothèque centrale	Projet Fin d'Etudes	Disponible	Electronique	Consultation sur place/Téléchargeable

Documents numériques

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