Abstract
The most advanced human–computer interaction is to make computers, like humans, capable of intelligent perception, judgment, and feedback. In the interaction, the emotions of the interactors can be identified to make intelligent measures. Emotion recognition mainly includes the recognition of speech, facial expressions, text, gestures, and physiological signals. Among them, emotional recognition in physiological signals is the most authentic. Since the EEG signal is a comprehensive reflection of the activities of many neurons in the brain in the cerebral cortex and can directly reflect brain activity, the EEG is rich in useful information. Therefore, this article uses EEG signals for the study of emotion recognition. First, the EEG is collected, preprocessed, and feature extracted; then an improved radial basis function neural network (I-RBF-NN) algorithm is used to process the EEG data; finally, the experimental results obtained by different classification models are compared and analyzed. The experimental results show that the I-RBF-NN proposed in this paper is better than other comparison algorithms for emotion recognition of EEG.
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