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2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) (2017)
Oxford, United Kingdom
July 26, 2017 to July 28, 2017
ISBN: 978-1-5386-0772-5
pp: 109-114
Benyu Zhang , Brain Cognitive Computing Lab., School of Information Engineering, Minzu University of China, Beijing, 100081, China
Huiping Jiang , Brain Cognitive Computing Lab., School of Information Engineering, Minzu University of China, Beijing, 100081, China
Linshan Dong , Brain Cognitive Computing Lab., School of Information Engineering, Minzu University of China, Beijing, 100081, China
ABSTRACT
With the continuous development of the computer, the brain computer interface system (BCI) has become an important part of computer research. Emotion recognition is an important task for computer to understand the human status in BCI. Affective computing (AC) aim to develop the model of emotions and advance the affective intelligence of computers. In this paper, we proposed a new method to better the classification of EEG signals in emotion recognition, in which, WT-CNN was used to extracting features and recognize two emotions (positive, negative) according to the output of wavelet transform on raw signal. The simulation shows that WT-CNN could obtain better results, and the best results could be reached to 88%.
INDEX TERMS
Emotion recognition, Feature extraction, Brain modeling, Computational modeling, Electroencephalography, Wavelet transforms
CITATION

B. Zhang, H. Jiang and L. Dong, "Classification of EEG signal by WT-CNN model in emotion recognition system," 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, United Kingdom, 2017, pp. 109-114.
doi:10.1109/ICCI-CC.2017.8109738
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