SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction

• URL: http://arxiv.org/abs/2104.06308v1
• Date: Fri, 9 Apr 2021 12:59:38 GMT
• Title: SFE-Net: EEG-based Emotion Recognition with Symmetrical Spatial Feature Extraction
• Authors: Xiangwen Deng, Shangming Yang and Junlin Zhu
• Abstract summary: We present a spatial folding ensemble network (SFENet) for EEG feature extraction and emotion recognition. Motivated by the spatial symmetry mechanism of human brain, we fold the input EEG channel data with five different symmetrical strategies. With this network, the spatial features of different symmetric folding signlas can be extracted simultaneously, which greatly improves the robustness and accuracy of feature recognition.
• Score: 1.8047694351309205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion recognition based on EEG (electroencephalography) has been widely used in human-computer interaction, distance education and health care. However, the conventional methods ignore the adjacent and symmetrical characteristics of EEG signals, which also contain salient information related to emotion. In this paper, we present a spatial folding ensemble network (SFENet) for EEG feature extraction and emotion recognition. Firstly, for the undetected area between EEG electrodes, we employ an improved Bicubic-EEG interpolation algorithm for EEG channel information completion, which allows us to extract a wider range of adjacent space features. Then, motivated by the spatial symmetry mechanism of human brain, we fold the input EEG channel data with five different symmetrical strategies: the left-right folds, the right-left folds, the top-bottom folds, the bottom-top folds, and the entire double-sided brain folding, which enable the proposed network to extract the information of space features of EEG signals more effectively. Finally, 3DCNN based spatial and temporal extraction and multi voting strategy of ensemble Learning are employed to model a new neural network. With this network, the spatial features of different symmetric folding signlas can be extracted simultaneously, which greatly improves the robustness and accuracy of feature recognition. The experimental results on DEAP and SEED data sets show that the proposed algorithm has comparable performance in term of recognition accuracy.

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