{"title":"消极情绪状态自动识别的脑电信号频谱特征","authors":"F. Feradov, T. Ganchev","doi":"10.1109/ET.2019.8878557","DOIUrl":null,"url":null,"abstract":"In the presented paper we investigate the properties of spectral EEG features for the detection of negative emotional states. In particular, the proposed features represent the dynamics of energy distribution in the frequency range of 20–35 Hz, based on a time-frequency analysis of multichannel EEG signal. The experimental evaluation is based on data from the DEAP database. We report results with J48- and SMO-based classifiers, in terms of average classification accuracy, 94.3% and 96.8%, respectively.","PeriodicalId":306452,"journal":{"name":"2019 IEEE XXVIII International Scientific Conference Electronics (ET)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Spectral Features of EEG Signals for the Automated Recognition of Negative Emotional States\",\"authors\":\"F. Feradov, T. Ganchev\",\"doi\":\"10.1109/ET.2019.8878557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the presented paper we investigate the properties of spectral EEG features for the detection of negative emotional states. In particular, the proposed features represent the dynamics of energy distribution in the frequency range of 20–35 Hz, based on a time-frequency analysis of multichannel EEG signal. The experimental evaluation is based on data from the DEAP database. We report results with J48- and SMO-based classifiers, in terms of average classification accuracy, 94.3% and 96.8%, respectively.\",\"PeriodicalId\":306452,\"journal\":{\"name\":\"2019 IEEE XXVIII International Scientific Conference Electronics (ET)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE XXVIII International Scientific Conference Electronics (ET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ET.2019.8878557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE XXVIII International Scientific Conference Electronics (ET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ET.2019.8878557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spectral Features of EEG Signals for the Automated Recognition of Negative Emotional States
In the presented paper we investigate the properties of spectral EEG features for the detection of negative emotional states. In particular, the proposed features represent the dynamics of energy distribution in the frequency range of 20–35 Hz, based on a time-frequency analysis of multichannel EEG signal. The experimental evaluation is based on data from the DEAP database. We report results with J48- and SMO-based classifiers, in terms of average classification accuracy, 94.3% and 96.8%, respectively.
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