期刊
TECHNOLOGY AND HEALTH CARE
卷 26, 期 -, 页码 S509-S519出版社
IOS PRESS
DOI: 10.3233/THC-174836
关键词
Emotion recognition; multi-channel EEG; DWT; Valence; Arousal
资金
- National Natural Science Foundation of China [61602017, 61420106005]
- National Basic Research Programme of China [2014CB744600]
- 'Rixin Scientist' Foundation of Beijing University of Technology [2017-RX(1)-03]
- Beijing Natural Science Foundation [4164080]
- Beijing Outstanding Talent Training Foundation [2014000020124G039]
- International Science and Technology Cooperation Program of China [2013DFA32180]
- Special fund of Beijing Municipal Science and Technology Commission [Z171100000117004, Z151100003915117]
- Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support [ZYLX201607]
- Beijing Municipal Administration of Hospitals Ascent Plan [DFL20151801]
BACKGROUND: Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. OBJECTIVE: This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. METHODS: We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. RESULTS: The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. CONCLUSIONS: This paper provided better frequency bands and channels reference for emotion recognition based on EEG.
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