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Since emotion can reflect information of hobbies, personality, interests and even health, recognition of human emotions can help machines and robots in improving the reliability of human-machine interaction ( Yin et al., 2017) and also help them in action processing and social cognition ( Urgen et al., 2013). Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.Įmotion has a great influence on human cognition ( Yoo et al., 2014), behavior and communication. The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified.

Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. The framework consists of a linear EEG mixing model and an emotion timing model. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). 2School of Information Science and Engineering, Lanzhou University, Lanzhou, ChinaĮEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans.