{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T14:32:55Z","timestamp":1771511575216,"version":"3.50.1"},"reference-count":49,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2019,3]]},"abstract":"<jats:p> The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1[Formula: see text]s to 12[Formula: see text]s segments, was 12[Formula: see text]s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification. <\/jats:p>","DOI":"10.1142\/s0129065718500442","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T02:44:06Z","timestamp":1537325046000},"page":"1850044","source":"Crossref","is-referenced-by-count":25,"title":["Identifying Suitable Brain Regions and Trial Size Segmentation for Positive\/Negative Emotion Recognition"],"prefix":"10.1142","volume":"29","author":[{"given":"Jennifer","family":"Sorinas","sequence":"first","affiliation":[{"name":"Institute of Bioengineering, University Miguel Hern\u00e1ndez and CIBER BBN, Avenida de la Universidad, Elche 03202, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Dolores","family":"Grima","sequence":"additional","affiliation":[{"name":"Telecomm School, Universidad Politecnica de Cartagena and Institute of Bioengineering, University Miguel Hern\u00e1ndez, Avenida de la Universidad Elche 03202, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose Manuel","family":"Ferrandez","sequence":"additional","affiliation":[{"name":"Telecomm School, Universidad Politecnica de Cartagena, Campus Muralla del Mar s\/n, Cartagena (Murcia) 30202, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo","family":"Fernandez","sequence":"additional","affiliation":[{"name":"Institute of Bioengineering, University Miguel Hern\u00e1ndez and CIBER BBN, Avenida de la Universidad, Elche 03202, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,2,21]]},"reference":[{"key":"S0129065718500442BIB001","doi-asserted-by":"publisher","DOI":"10.1126\/science.1076358"},{"key":"S0129065718500442BIB002","volume-title":"Human Emotions","author":"Izard C. 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