| Titre : |
Multimodal neurophysiological signals analysis for stress assessment |
| Type de document : |
document électronique |
| Auteurs : |
Israa Boulefaat, Auteur ; Mohamed Abdelhadi Cherfouhi, Auteur ; Taous Meriem Laleg, Directeur de thèse ; Latifa Hamami, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2025 |
| Importance : |
1 fichier PDF (20.2 Mo) |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 114-117. - Annexes |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Neurophysiological signals
Multimodality
Machine learning
Image processing |
| Index. décimale : |
PN00425 |
| Résumé : |
This project investigates the use of neurophysiological signals, specifically EEG, ECG, and PPG, for stress assessment in 23 participants performing various tasks, with corresponding stress levels recorded for each activity. The signals are segmented and filtered, then converted into images using two distinct techniques: Visibility Graph and Gramian Angular Field Image Representations.
This multimodal approach enables the integration of diverse and complementary information from different physiological sources. Feature extraction is subsequently performed using two complementary strategies: Wavelet Packet Transform combined with Zernike and Hu Moments, and Semi-Classical Signal Analysis (SCSA).
Once the full processing pipeline is completed, a supervised machine learning model is trained using the stress labels in order to evaluate and compare the performance of each feature extraction method. For each signal type, the most effective strategy is selected, and their outputs are then fused to enhance the overall performance of the stress assessment system by leveraging the benefits of multimodality. |
Multimodal neurophysiological signals analysis for stress assessment [document électronique] / Israa Boulefaat, Auteur ; Mohamed Abdelhadi Cherfouhi, Auteur ; Taous Meriem Laleg, Directeur de thèse ; Latifa Hamami, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (20.2 Mo).
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 114-117. - Annexes Langues : Anglais ( eng)
| Mots-clés : |
Neurophysiological signals
Multimodality
Machine learning
Image processing |
| Index. décimale : |
PN00425 |
| Résumé : |
This project investigates the use of neurophysiological signals, specifically EEG, ECG, and PPG, for stress assessment in 23 participants performing various tasks, with corresponding stress levels recorded for each activity. The signals are segmented and filtered, then converted into images using two distinct techniques: Visibility Graph and Gramian Angular Field Image Representations.
This multimodal approach enables the integration of diverse and complementary information from different physiological sources. Feature extraction is subsequently performed using two complementary strategies: Wavelet Packet Transform combined with Zernike and Hu Moments, and Semi-Classical Signal Analysis (SCSA).
Once the full processing pipeline is completed, a supervised machine learning model is trained using the stress labels in order to evaluate and compare the performance of each feature extraction method. For each signal type, the most effective strategy is selected, and their outputs are then fused to enhance the overall performance of the stress assessment system by leveraging the benefits of multimodality. |
|
PN00100 
Ajouter le résultat dans votre panier Faire une suggestion Affiner la rechercheMultimodal neurophysiological signals analysis for stress assessment (2025)
