Classification of Music-Evoked Emotions from EEG signals using Self-Organizing Maps
The use of music to address some emotional, cognitive, and physical needs of an individual, as proposed by Music therapy, requires to at least identify the emotional expression that a piece of music evoke in people. The aim of this study is to build a computational system to identify emotions as tem...
Gespeichert in:
| Veröffentlicht in: | 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) S. 1 - 6 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
20.07.2022
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The use of music to address some emotional, cognitive, and physical needs of an individual, as proposed by Music therapy, requires to at least identify the emotional expression that a piece of music evoke in people. The aim of this study is to build a computational system to identify emotions as temporal representations from Electroencephalography (EEG) signals. First, a set of eight pieces of music are composed to theoretically evoke four emotions in people: happiness, sadness, calmness and anger. Then, EEG signals from 30 participants are recorded, first when they remain silence for one minute (baseline) and then while they are listening the stimuli (the eight pieces of music). After listening each stimulus, participants provide a score in terms of Arousal and Valence through the self-assessment manikin test (SAM). The EEGLife dataset is built by reducing noise and cleaning artifacts from raw EEG signals and using the participant's scores. A set of Self-Organizing Maps (the SOM model) is proposed to classify emotions. A subject-specific training schema with 20% per participant for validation is used not only for the EEGLife dataset, but also for the well-known benchmark DEAP dataset. Cross-correlation and bandpower features are extracted from both datasets to feed the model. A Density Peaks clustering algorithm and a fuzzy system are used to find the final emotional scores for the model. Precision, Recall, F1Score and Accuracy metrics are used to assess the performance of the proposed model. Results show that emotional expression information is extracted easier from EEGLife than DEAP dataset. Finally, the SOM model has an interpretability property, which allows to visually analyze EEG signals in Self-Organizing Maps. |
|---|---|
| AbstractList | The use of music to address some emotional, cognitive, and physical needs of an individual, as proposed by Music therapy, requires to at least identify the emotional expression that a piece of music evoke in people. The aim of this study is to build a computational system to identify emotions as temporal representations from Electroencephalography (EEG) signals. First, a set of eight pieces of music are composed to theoretically evoke four emotions in people: happiness, sadness, calmness and anger. Then, EEG signals from 30 participants are recorded, first when they remain silence for one minute (baseline) and then while they are listening the stimuli (the eight pieces of music). After listening each stimulus, participants provide a score in terms of Arousal and Valence through the self-assessment manikin test (SAM). The EEGLife dataset is built by reducing noise and cleaning artifacts from raw EEG signals and using the participant's scores. A set of Self-Organizing Maps (the SOM model) is proposed to classify emotions. A subject-specific training schema with 20% per participant for validation is used not only for the EEGLife dataset, but also for the well-known benchmark DEAP dataset. Cross-correlation and bandpower features are extracted from both datasets to feed the model. A Density Peaks clustering algorithm and a fuzzy system are used to find the final emotional scores for the model. Precision, Recall, F1Score and Accuracy metrics are used to assess the performance of the proposed model. Results show that emotional expression information is extracted easier from EEGLife than DEAP dataset. Finally, the SOM model has an interpretability property, which allows to visually analyze EEG signals in Self-Organizing Maps. |
| Author | Suarez, Miguel Ojeda, Andres Leon, Elizabeth Gomez, Jonatan Beltran-Velandia, Ferney |
| Author_xml | – sequence: 1 givenname: Ferney surname: Beltran-Velandia fullname: Beltran-Velandia, Ferney email: fbeltranv@unal.edu.co organization: A life and MIDAS Research Groups, Universidad Nacional de Colombia,Colombia,Bogotá – sequence: 2 givenname: Jonatan surname: Gomez fullname: Gomez, Jonatan organization: A life and MIDAS Research Groups, Universidad Nacional de Colombia,Colombia,Bogotá – sequence: 3 givenname: Miguel surname: Suarez fullname: Suarez, Miguel organization: A life and MIDAS Research Groups, Universidad Nacional de Colombia,Colombia,Bogotá – sequence: 4 givenname: Andres surname: Ojeda fullname: Ojeda, Andres organization: A life and MIDAS Research Groups, Universidad Nacional de Colombia,Colombia,Bogotá – sequence: 5 givenname: Elizabeth surname: Leon fullname: Leon, Elizabeth organization: A life and MIDAS Research Groups, Universidad Nacional de Colombia,Colombia,Bogotá |
| BookMark | eNotj01qwzAYRFVoF03SE2SjC9iVZP0ui1HTQEIW8T7I9icjYkvBagvt6ZvQrIY3PAZmgR5jioAQpqSklJjXbW1r2wghmCoZYaw0WjHF9ANaUCkFV0Qr84yO9ehyDj507jOkiJPH-68cusJ-pzP02E7p1mfs5zRhazc4hyG6MeOrFQd8hNEXh3lwMfzeeO8ueYWe_NWAl3suUfNum_qj2B022_ptVwSueeF6aaQ0XGjTMWZaplrhORgqK-2F02C0Bl5532luJBDOBWkZ69q-leCor5Zo_T8bAOB0mcPk5p_T_Wb1B7TfTa8 |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICECET55527.2022.9872728 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Music |
| EISBN | 1665470879 9781665470872 |
| EndPage | 6 |
| ExternalDocumentID | 9872728 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Ministry of Education funderid: 10.13039/501100002701 – fundername: Universidad Nacional de Colombia funderid: 10.13039/501100002753 |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i484-ad696694589c229b27b5f4e91638f5a8e988e43ffc8496e04450b22cbdb6ea1f3 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jan 18 11:14:22 EST 2024 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i484-ad696694589c229b27b5f4e91638f5a8e988e43ffc8496e04450b22cbdb6ea1f3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_9872728 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-July-20 |
| PublicationDateYYYYMMDD | 2022-07-20 |
| PublicationDate_xml | – month: 07 year: 2022 text: 2022-July-20 day: 20 |
| PublicationDecade | 2020 |
| PublicationTitle | 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) |
| PublicationTitleAbbrev | ICECET |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8010248 |
| Snippet | The use of music to address some emotional, cognitive, and physical needs of an individual, as proposed by Music therapy, requires to at least identify the... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Affective Computing Analytical models Brain modeling Computational modeling EEG signals Feature extraction Medical treatment Music Self-organizing feature maps Self-Organizing Maps |
| Title | Classification of Music-Evoked Emotions from EEG signals using Self-Organizing Maps |
| URI | https://ieeexplore.ieee.org/document/9872728 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEJ4AevDkA4zv9ODRwtLt7rZnsqgHCQkcuJFtOzVEwxJev9-2u2JMvHjrK2kybTOd9vvmA3hUUREzaVLat7GmXGNClXV7mfPMGne4jEQdxCay0UjMZnLcgKcDFwYRA_gMu74Y_vJNqXf-qazn4mOWMdGEZpalFVfrG5wTyd7rIB_k08SnFHNxH2Pdevgv3ZTgNoan_5vwDDo__DsyPniWc2jg8gKOgiRzGyZBx9IjfIJRSWlJ6KH5vvxAQ_JKmGdDPHOE5Pkz8RgNt8uIx7i_kwl-WlpTMH39rVhtOjAd5tPBC62lEeiCC04Lk7owRfJESM2YVCxTieUo_eXKJoVAKQTy2FotuEwx4jyJFGNaGZVi4ZblElrLcolXQEQ_ZTzWqcG44JnSBSam7wrWBa-xux5dQ9vbZb6qkl_Ma5Pc_N18Cyfe9P7xk0V30Nqud3gPx3q_XWzWD2HFvgDAr5iw |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LbwIhECZWm7SnPrTpuxx6LLqy7C6czVpN1Zi4B29mgaExbVzj6_cXcGvTpJfeeISQzAyBge_jQ-hZBnlIhY5J24SKMAURkcbGMmOJ0XZxaQHKi00koxGfTsW4gl4OXBgA8OAzaLqif8vXhdq6q7KWzY9pQvkRqkWM0WDP1vqG5wSi1e-knTSL3KdiNvOjtFkO-KWc4jeO7tn_pjxHjR8GHh4f9pYLVIHFJap5UeY6mnglS4fx8WbFhcG-h6S74gM0TvfSPGvsuCM4TV-xQ2nYOMMO5f6OJ_BpSEnCdPVhvlw3UNZNs06PlOIIZM44I7mObaIiWMSFolRImsjIMBDueGWinIPgHFhojOJMxBAwFgWSUiW1jCG3jrlC1UWxgGuEeTumLFSxhjBniVQ5RLptC8amr6E9IN2gurPLbLn__mJWmuT27-YndNLLhoPZoD96u0Onzg3uKpQG96i6WW3hAR2r3Wa-Xj16730B4h-b9w |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+International+Conference+on+Electrical%2C+Computer+and+Energy+Technologies+%28ICECET%29&rft.atitle=Classification+of+Music-Evoked+Emotions+from+EEG+signals+using+Self-Organizing+Maps&rft.au=Beltran-Velandia%2C+Ferney&rft.au=Gomez%2C+Jonatan&rft.au=Suarez%2C+Miguel&rft.au=Ojeda%2C+Andres&rft.date=2022-07-20&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICECET55527.2022.9872728&rft.externalDocID=9872728 |