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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.<\/jats:p>","DOI":"10.1007\/978-3-031-09034-9_35","type":"book-chapter","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T06:02:13Z","timestamp":1701928933000},"page":"323-331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology"],"prefix":"10.1007","author":[{"given":"Duarte","family":"Rodrigues","sequence":"first","affiliation":[]},{"given":"Luis Paulo","family":"Reis","sequence":"additional","affiliation":[]},{"given":"Br\u00edgida M\u00f3nica","family":"Faria","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"key":"35_CR1","unstructured":"IntellWheels2.0 \u2013 Intelligent Wheelchair with Flexible Multimodal Interface and Realistic Simulator. 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