EEGbased emotion recognition using simple recurrent units network and ensemble learning Abstract   The purpose of this research is to develop an EEGbased 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 longterm dependencies occurrence in normal Recurrent Neural Network (RNN). Before training the emotion models, Dualtree Complex Wavelet Transform (DTCWT) was applied to decompose the original EEG into five constituent subbands, 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.

Shorttimespan EEGbased personalized emotion recognition with deep convolutional neural network Emotion recognition can be useful in various applications such as in neurofeedback training for functional enhancement. A practically realizable emotion recognition system should rely on as little physiological signalsmodalities as possible. Also, emotionrelated neurological activities may be vastly different from person to person. Hence, this paper presents the singlemodal EEGbased personalized emotion recognition convolutional neural network (CNN) models working on the DEAP dataset. The valence and arousal level classification performance of our presented CNN classifiers have surpassed the other emotion classifiers working on the DEAP dataset based on our scope of literature reviewed. The models, which are deep CNN, rely on only plain EEG data and require no preextracted EEG features. The design and application of the CNN models is aimed at possible future work of identification of new emotionrelated EEG features, relying on the automated feature extraction capability of the CNN. The two CNN models presented have achieved the 3class valence classification test accuracy of 97.59% and 98.75% respectively, and the 3class arousal classification test accuracy of 98.48% and 97.58%.