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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n                    Affect decoding through brain-computer interfacing (BCI) holds great potential to capture users\u2019 feelings and emotional responses via non-invasive electroencephalogram (EEG) sensing. Yet, little research has been conducted to understand\n                    <jats:italic>efficient<\/jats:italic>\n                    decoding when users are exposed to\n                    <jats:italic>dynamic<\/jats:italic>\n                    audiovisual contents. In this regard, we study EEG-based affect decoding from videos in arousal and valence classification tasks, considering the impact of signal length, window size for feature extraction, and frequency bands. We train both classic Machine Learning models (SVMs and\n                    <jats:italic>k<\/jats:italic>\n                    -NNs) and modern Deep Learning models (FCNNs and GTNs). Our results show that: (1)\u00a0affect can be effectively decoded using less than 1 minute of EEG signal; (2)\u00a0temporal windows of 6 and 10 seconds provide the best classification performance for classic Machine Learning models but Deep Learning models benefit from much shorter windows of 2 seconds; and (3)\u00a0any model trained on the Beta band alone achieves similar (sometimes better) performance than when trained on all frequency bands. Taken together, our results indicate that affect decoding can work in more realistic conditions than currently assumed, thus becoming a viable technology for creating better interfaces and user models.\n                  <\/jats:p>","DOI":"10.1145\/3663669","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T07:56:37Z","timestamp":1714722997000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Efficient Decoding of Affective States from Video-elicited EEG Signals: An Empirical Investigation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6172-0560","authenticated-orcid":false,"given":"Kayhan","family":"Latifzadeh","sequence":"first","affiliation":[{"name":"University of Luxembourg, Esch-sur-Alzette, Luxembourg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6996-1673","authenticated-orcid":false,"given":"Nima","family":"Gozalpour","sequence":"additional","affiliation":[{"name":"University of Luxembourg, Esch-sur-Alzette, Luxembourg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1596-8466","authenticated-orcid":false,"given":"V. 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