caa a22 4500 606159673 CHVBK 20210128100626.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1653-6 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1653-6 Learning to decode human emotions from event-related potentials [Elektronische Daten] [O. Georgieva, S. Milanov, P. Georgieva, I. Santos, A. Pereira, C. Silva] Reported works on electroencephalogram (EEG)-based emotion recognition systems generally employ the principles of supervised learning to build subject-dependent (single/intra-subject) models. Building subject-independent (multiple/inter-subject) models is a harder problem due to the EEG data variability between subjects. The contribution of this paper is twofold. First, we provide a framework for selection of a small number of basic temporal features, event-related potential (ERP) amplitudes, and latencies that are sufficiently robust to discriminate emotion states across multiple subjects. Second, we test comparatively the feasibility of six standard unsupervised (clustering) techniques to build intra-subject and inter-subject models to discriminate emotion valence in the ERPs collected while subjects were viewing high arousal images with positive or negative emotional content. The Natural Computing Applications Forum, 2014 Affective computing nationallicence Emotion valence recognition nationallicence Feature reduction nationallicence Clustering nationallicence Event-related potentials (ERPs) nationallicence Georgieva O. FMI, Sofia University "St. Kl. Ohridski”, Sofia, Bulgaria aut Milanov S. FMI, Sofia University "St. Kl. Ohridski”, Sofia, Bulgaria aut Georgieva P. DETI/IEETA, University of Aveiro, 3810-193, Aveiro, Portugal aut Santos I. Dept. de Educação, University of Aveiro, 3810-193, Aveiro, Portugal aut Pereira A. Dept. de Educação, University of Aveiro, 3810-193, Aveiro, Portugal aut Silva C. Dept. de Educação, University of Aveiro, 3810-193, Aveiro, Portugal aut Neural Computing and Applications Springer London 26/3(2015-04-01), 573-580 0941-0643 26:3<573 2015 26 521 https://doi.org/10.1007/s00521-014-1653-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1653-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Georgieva O. FMI, Sofia University "St. Kl. Ohridski”, Sofia, Bulgaria aut NATIONALLICENCE 700 1- Milanov S. FMI, Sofia University "St. Kl. Ohridski”, Sofia, Bulgaria aut NATIONALLICENCE 700 1- Georgieva P. DETI/IEETA, University of Aveiro, 3810-193, Aveiro, Portugal aut NATIONALLICENCE 700 1- Santos I. Dept. de Educação, University of Aveiro, 3810-193, Aveiro, Portugal aut NATIONALLICENCE 700 1- Pereira A. Dept. de Educação, University of Aveiro, 3810-193, Aveiro, Portugal aut NATIONALLICENCE 700 1- Silva C. Dept. de Educação, University of Aveiro, 3810-193, Aveiro, Portugal aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 573-580 0941-0643 26:3<573 2015 26 521