1. Emotion Recognition and Channel Selection Based on EEG Signal
- Author
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Laiyuan Tong, Jinchuang Zhao, and Wenli Fu
- Subjects
medicine.diagnostic_test ,Computer science ,business.industry ,Speech recognition ,020208 electrical & electronic engineering ,02 engineering and technology ,Electroencephalography ,Automation ,Field (computer science) ,Wavelet ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,medicine ,020201 artificial intelligence & image processing ,Time domain ,business ,Wearable technology ,Communication channel - Abstract
As an important research direction in the field of artificial intelligence, emotion recognition has become a hot topic in current research. Because of the use of multi-channel EEG acquisition equipment nowadays, which brings many problems in practical use and post-calculation, we have also studied channel selection. In this paper, the DEAP database is used as EEG data. The multi-feature fusion in a time domain and the composite features based on wavelet feature and information entropy are used as EEG features for emotion recognition. The average recognition accuracy reached 72.03% and 71.7% respectively. We also use the ReliefF algorithm to select EEG channels. Under the premise of a slight loss of emotional recognition preparation rate, we selected the optimal combination of 6 and 13 channels, and the brain data was reduced from 32 channels to 13 channels. It lays a foundation for the development of portable, wearable devices.
- Published
- 2018
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