A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
2022
Ziyi Lv | Jing Zhang | Estanislao Epota Oma
EEG-based emotion recognition research has become a hot research topic. However, many studies focus on identifying emotional states from time domain features, frequency domain features, and time-frequency domain features of EEG signals, ignoring the spatial information and frequency band characteristics of the EEG signals. In this paper, an emotion recognition method based on multi-band EEG topology maps is proposed by combining the frequency domain features, spatial information, and frequency band characteristics of multi-channel EEG signals. In this method, multi-band EEG topology maps are introduced to present EEG signals, and a novel emotion recognition network, ERENet, is proposed to recognize emotional states from multi-band EEG topology maps. The results on the DEAP dataset show that the performance of ERENet outperforms that of most of the current methods.
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