Temporal Analysis of Functional Brain Connectivity for EEG-based Emotion Recognition

Document Type : Article

Authors

Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran

Abstract

EEG signals in emotion recognition absorb special attention owing to their high temporal resolution and their information about brain activity. Different brain areas work together and the activity of brain changes over time. In this study, we investigate the emotion classification performance using functional connectivity features in different frequency bands and compare them with the classification performance using differential entropy feature, which has been previously used for this task. Moreover, we investigate the effect of different time periods on classification performance. Our results on SEED dataset show that as time goes on, emotions become more stable and the classification accuracy increases. Among different time periods, we achieve the highest classification accuracy using the time period of 140s-end. In this time period, the accuracy is improved by 4 to 6% compared to the entire signal. Pearson correlation coefficient, coherence and phase locking value features and SVM obtain the mean accuracy of about 88%. Using the proposed framework, functional connectivity features lead to better classification accuracy than DE features (with the mean accuracy of 84.89%). Finally, using the best time interval and SVM, we achieve better accuracy than using RNNs which need large amount of data and have high computational cost.

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Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 21 February 2024

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