{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:13:37Z","timestamp":1761808417866,"version":"build-2065373602"},"reference-count":80,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Based on the growing interest in encephalography to enhance human\u2013computer interaction (HCI) and develop brain\u2013computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band\u2014electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions\u2014valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20\u20138.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.<\/jats:p>","DOI":"10.3390\/s20247083","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T08:59:34Z","timestamp":1607590774000},"page":"7083","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hybrid Method of Automated EEG Signals\u2019 Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6124-1236","authenticated-orcid":false,"given":"Agnieszka","family":"Wosiak","sequence":"first","affiliation":[{"name":"Institute of Information Technology, Lodz University of Technology, W\u00f3lcza\u0144ska 215, 90-924 \u0141\u00f3d\u017a, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0428-017X","authenticated-orcid":false,"given":"Aleksandra","family":"Dura","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Lodz University of Technology, W\u00f3lcza\u0144ska 215, 90-924 \u0141\u00f3d\u017a, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1007\/s12539-018-0292-5","article-title":"Study on feature selection methods for depression detection using three-electrode EEG data","volume":"10","author":"Cai","year":"2018","journal-title":"Interdiscip. 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