Publication
Emotion classification based on single electrode brain data: applications for assistive technology
| dc.contributor.author | Rodrigues, Duarte | |
| dc.contributor.author | Reis, Luís Paulo | |
| dc.contributor.author | Faria, Brígida Mónica | |
| dc.contributor.author | Faria, Brígida Mónica | |
| dc.date.accessioned | 2025-03-27T11:12:50Z | |
| dc.date.available | 2025-03-27T11:12:50Z | |
| dc.date.issued | 2023-12-08 | |
| dc.description.abstract | This research case focused on the development of an emotion classification system aimed to be integrated in projects committed to improve assistive technologies. An experimental protocol was designed to acquire an electroencephalogram (EEG) signal that translated a certain emotional state. To trigger this stimulus, a set of clips were retrieved from an extensive database of pre-labeled videos. Then, the signals were properly processed, in order to extract valuable features and patterns to train the machine and deep learning models.There were suggested 3 hypotheses for classification: recognition of 6 core emotions; distinguishing between 2 different emotions and recognising if the individual was being directly stimulated or merely processing the emotion. Results showed that the first classification task was a challenging one, because of sample size limitation. Nevertheless, good results were achieved in the second and third case scenarios (70% and 97% accuracy scores, respectively) through the application of a recurrent neural network. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Rodrigues, D., Reis, L. P., & Faria, B. M. (2023). Emotion classification based on single electrode bbrain data: Applications for assistive technology. Em P. Brito, J. G. Dias, B. Lausen, A. Montanari, & R. Nugent (Eds.), Classification and Data Science in the Digital Age (IFCS 2022) (pp. 323–331). Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-09034-9_35 | pt_PT |
| dc.identifier.eissn | 2198-3321 | |
| dc.identifier.isbn | 978-3-031-09033-2; EISBN: 978-3-031-09034-9 | |
| dc.identifier.issn | 1431-8814 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/29885 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | |
| dc.publisher | Springer Nature | pt_PT |
| dc.relation | UIDB/00027/2020; NORTE-01-0247- FEDER-039720 | |
| dc.relation.hasversion | https://link.springer.com/chapter/10.1007/978-3-031-09034-9_35 | |
| dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-031-09034-9_35 | pt_PT |
| dc.rights.uri | N/A | |
| dc.subject | Emotions | pt_PT |
| dc.subject | Brain-computer interface | pt_PT |
| dc.subject | EEG | pt_PT |
| dc.subject | Supervised learning | pt_PT |
| dc.subject | Machine and deep learning | pt_PT |
| dc.title | Emotion classification based on single electrode brain data: applications for assistive technology | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 331 | pt_PT |
| oaire.citation.startPage | 323 | pt_PT |
| oaire.citation.title | Classification and Data Science in the Digital Age (IFCS 2022) | pt_PT |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Faria | |
| person.givenName | Brigida Monica | |
| person.identifier | R-000-T1F | |
| person.identifier.ciencia-id | 0D1F-FB5E-55E4 | |
| person.identifier.orcid | 0000-0003-2102-3407 | |
| person.identifier.rid | C-6649-2012 | |
| person.identifier.scopus-author-id | 6506476517 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 85832a40-7ef9-431a-be0c-78b45ebbae86 | |
| relation.isAuthorOfPublication.latestForDiscovery | 85832a40-7ef9-431a-be0c-78b45ebbae86 |