  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, a spatial folding ensemble network (SFE-Net) is
presented for EEG feature extraction and emotion recognition. Firstly, for the
undetected area between EEG electrodes, an improved Bicubic-EEG interpolation
algorithm is developed for EEG channels information completion, which allows us
to extract a wider range of adjacent space features. Then, motivated by the
spatial symmetric mechanism of human brain, we fold the input EEG channels data
with five different symmetrical strategies, which enable the proposed network
to extract the information of space features of EEG signals more effectively.
Finally, a 3DCNN-based spatial, temporal extraction, and a multi-voting
strategy of ensemble learning are integrated to model a new neural network.
With this network, the spatial features of different symmetric folding signals
can be extracted simultaneously, which greatly improves the robustness and
accuracy of emotion recognition. The experimental results on DEAP and SEED
datasets show that the proposed algorithm has comparable performance in terms
of recognition accuracy.
