ELINA: Emotion low-resources INference algorithm based on 1D ConvNets
In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven dist...
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| Published in | Journal of intelligent & fuzzy systems |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
28.03.2024
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| Online Access | Get full text |
| ISSN | 1064-1246 1875-8967 |
| DOI | 10.3233/JIFS-219334 |
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| Abstract | In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of 84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals. |
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| AbstractList | In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of 84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals. |
| Author | Cardoso-Moreno, Marco A. Yáñez-Márquez, Cornelio Luján-García, Juan Eduardo |
| Author_xml | – sequence: 1 givenname: Marco A. surname: Cardoso-Moreno fullname: Cardoso-Moreno, Marco A. organization: Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, esq. Miguel Othón de Mendizábal, Col.Nueva Industrial Vallejo, Alcaldıa Gustavo A. Madero, C.P. 07700, CDMX – sequence: 2 givenname: Juan Eduardo surname: Luján-García fullname: Luján-García, Juan Eduardo organization: Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, esq. Miguel Othón de Mendizábal, Col.Nueva Industrial Vallejo, Alcaldıa Gustavo A. Madero, C.P. 07700, CDMX, Biomedical Informatics Group (GIB), Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, Madrid, Spain – sequence: 3 givenname: Cornelio surname: Yáñez-Márquez fullname: Yáñez-Márquez, Cornelio organization: Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, esq. Miguel Othón de Mendizábal, Col.Nueva Industrial Vallejo, Alcaldıa Gustavo A. Madero, C.P. 07700, CDMX |
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| Cites_doi | 10.1109/TAFFC.2016.2625250 10.3390/s140611031 10.17977/um018v4i22021p145-152 10.7202/014718ar 10.3390/s20030592 10.1016/j.imu.2020.100363 10.3390/s21123961 10.21105/joss.02977 10.1109/CSPA.2011.5759912 10.1007/978-3-319-24574-4_28 10.3390/s20123510 10.20965/jaciii.2023.p0967 10.1038/s41598-019-42826-2 10.1109/ACCESS.2019.2891579 10.3390/electronics12132795 10.1016/j.ymssp.2020.107398 10.1109/REEDCON57544.2023.10151406 10.1098/rspa.1998.0193 10.3233/THC-174747 10.1016/j.bspc.2019.101646 10.1109/TAFFC.2018.2884461 10.3390/s21155015 10.3390/s19204495 10.1016/j.bspc.2022.104456 10.1016/j.entcs.2019.04.009 10.3390/app11114945 10.1007/978-3-031-47640-2_16 10.1109/5.726791 |
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