Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network
Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2023-10-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/98789/download/pdf/ |
| _version_ | 1797645817492799488 |
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| author | Samar Bouazizi Emna Benmohamed Hela Ltifi |
| author_facet | Samar Bouazizi Emna Benmohamed Hela Ltifi |
| author_sort | Samar Bouazizi |
| collection | DOAJ |
| description | Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG. |
| first_indexed | 2024-03-11T14:52:22Z |
| format | Article |
| id | doaj.art-ed3f821b4f8e4341864b86336c310f21 |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-03-11T14:52:22Z |
| publishDate | 2023-10-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-ed3f821b4f8e4341864b86336c310f212023-10-30T09:11:03ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682023-10-0129101116113810.3897/jucs.9878998789Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State NetworkSamar Bouazizi0Emna Benmohamed1Hela Ltifi2Research Groups in Intelligent Machines LabOnaizah CollegesResearch Groups in Intelligent Machines LabEmotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion classification and feature extraction. Furthermore, we feed the selected features to a grouped ESN for recognizing emotions. Our approach is validated on the well-known DEAP benchmark, which includes the EEG data from 32 participants. The proposed model achieved 89.32% accuracy for H/L Arousal and 91.21% accuracy for H/L Valence on the DEAP dataset. The obtained results demonstrate the effectiveness of our approach, which yields good performance compared to existing models of emotion analysis based on EEG.https://lib.jucs.org/article/98789/download/pdf/EEG signalsRecognitionEcho State Networks (ESN |
| spellingShingle | Samar Bouazizi Emna Benmohamed Hela Ltifi Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network Journal of Universal Computer Science EEG signals Recognition Echo State Networks (ESN |
| title | Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network |
| title_full | Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network |
| title_fullStr | Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network |
| title_full_unstemmed | Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network |
| title_short | Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network |
| title_sort | enhancing eeg based emotion recognition using psd grouped deep echo state network |
| topic | EEG signals Recognition Echo State Networks (ESN |
| url | https://lib.jucs.org/article/98789/download/pdf/ |
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