Automated emotion recognition based on higher order statistics and deep learning algorithm Abstract   The objective of this paper is online recognition of human emotions based on electroencephalogram (EEG) signals. The emotions are originated from the central and peripheral nervous systems. Hence, it can be adequately characterized by the EEG signal, as it directly reflects changes in the human emotional states. This paper describes an automated classification of emotionslabeled EEG signals using nonlinear higher order statistics and deep learning algorithm. The discrete wavelet transform is used to decompose the studied signal into subbands, known as rhythms of the EEG signal. The thirdorder cumulants (ToC) are used to explore the nonlinear dynamics of each subband signal in higher dimensional space. The data in the higher dimensional space contain repeated and redundant information due to presence of various symmetries in the ToC. Hence, an evolutionary data reduction technique, namely, the particle swarm optimization, is employed to get rid of irrelevant information. The long shortterm memory based deep learning technique is used to retrieve the emotion variation from the optimized data corresponding to the labeled EEG signals. This study is carried out with the webavailable DEAP dataset that yields 82.01% average classification accuracy with 10fold crossvalidation technique corresponding to fourlabeled emotions classes. The achieved results have confirmed that the proposed algorithm has the potential for accurate and rapid recognition of human emotions.

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%.