Using EEG Effective Connectivity Based on Granger Causality and Directed Transfer Function for Emotion Recognition

Emotion is a complex phenomenon that originates from everyday issues and has significant effects on individual decisions. Electroencephalography (EEG) is one of the widely used tools in examining the neural correlates of emotions. In this research, two concepts of Granger causality and directional transfer function were utilized to analyze EEG data recorded from 36 healthy volunteers in positive, negative and neutral emotional states and determine the effective connectivity between different brain sources (obtained through independent component analysis). Shannon entropy was utilized to sort the brain sources obtained by the ICA method, and average topography helps to add spatial information to the proposed connectivity models. According to the obtained confusion matrix, our method yielded an overall accuracy of 75% in recognizing three emotional states. Positive emotion was recognized with the highest accuracy of 87.96% (precision = 0.78, recall = 0.78 and F1-score = 0.81), followed by neutral (accuracy = 82.41%) and negative (accuracy = 79.63%) emotions. Indeed, our proposed method achieved the highest recognition accuracy for positive emotion. The proposed model in the present study has the ability to identify emotions in a completely personalized way based on neurobiological data. In the future, the proposed approach in the present study can be integrated with machine learning and neural network methods.

Automated summary

Emotion is a complex phenomenon that has significant effects on individual decisions. This study analyzed EEG data obtained from 36 participants to determine the effective connectivity between brain sources in different emotional states. The proposed model achieved high accuracy in recognizing positive emotions and has the potential to be integrated with machine learning and neural network methods.