EEG-based emotion recognition using simple recurrent units network and ensemble learning

The purpose of this research is to develop an EEG-based emotion recognition system for identification of three emotions: positive, neutral and negative. Up to now, various modeling approaches for automatic emotion recognition have been reported. However, the time dependency property during emotion process has not been fully considered. In order to grasp the temporal information of EEG, we adopt deep Simple Recurrent Units (SRU) network which is not only capable of processing sequence data but also has the ability to solve the problem of long-term dependencies occurrence in normal Recurrent Neural Network (RNN). Before training the emotion models, Dual-tree Complex Wavelet Transform (DT-CWT) was applied to decompose the original EEG into five constituent sub-bands, from which features were then extracted using time, frequency and nonlinear analysis. Next, deep SRU models were established using four different features over five frequency bands and favorable results were found to be related to higher frequency bands. Finally, three ensemble strategies were employed to integrate base SRU models to get more desirable classification performance. We evaluate and compare the performance of shallow models, deep models and ensemble models. Our experimental results demonstrated that the proposed emotion recognition system based on SRU network and ensemble learning could achieve satisfactory identification performance with relatively economic computational cost.