Emotion Recognition Method Based on Dynamic Brain Network Feature

Emotion recognition is one of the most important frontier research topics in the field of Human-Computer Interaction (HCI) emotional intelligence. However, at present, Electroencephalogram (EEG)-based emotion recognition extracts static features and cannot mine the dynamic characteristics of emotions, and it is difficult to improve the emotion recognition ability. In current research on the construction of dynamic Brain Functional Networks (dBFNs) using EEG signals, a sliding window is usually used to form a dBFN by sequentially constructing a functional connectivity network in different windows. However, this method is limited by subjectively setting the window length and cannot extract the connection mode of the emotional state at each time point. Therefore, the loss of temporal information leads to the loss of brain connection information. To solve these problems, this study proposes a dynamic Phase Linearity Measurement (dyPLM) method that can adaptively construct an emotion-related brain network at each time point without sliding windows to characterize the dynamic characteristics of emotions. In addition, a Convolutional Neural Gate Recurrent Unit (CNGRU) emotion recognition model is proposed that could further extract the deep-seated features of the dynamic brain network and effectively improve the accuracy of emotion recognition. In experiments on the public emotion recognition EEG dataset, Database for Emotion Analysis using Physiological signals (DEAP), the four-class classification accuracy is as high as 99.71%, and the recognition accuracy is improved by 3.51 percentage points compared to MFBPST-3D-DRLF. On the SJTU Emotion EEG Dataset (SEED), the three-class classification accuracy is 99.99%, and the recognition accuracy is improved by 3.32 percentage points compared to MFBPST-3D-DRLF. This study demonstrates the effectiveness and practicality of the proposed methods.