EEG Functional Connectivity Predicts Continuous Fatigue Levels During Underload Task

• Published in: 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) pp. 322 - 327
• Main Author: Zhu, He-Xuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2021
• Subjects: Correlation coefficient; EEG; Estimation; Fatigue; Functional connectivity; Neural Network; Neural networks; Predictive models; Time series analysis; Training
• DOI: 10.1109/AIEA53260.2021.00075

We built a data-driven prediction model based on Time Series Neural Network regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the continuous fatigue levels during underload task. When participants performed a 120-minute dull driving task, experimental data was recorded. The EEG and eye closure data used in this paper are from SJTU Emotion EEG Dataset, SEED. Unlike other researches, We proposed a functional brain networks method based on Phase Lag Index (PLI) to analyze EEG effectively and incorporate the temporal dependency of PERCLOS into model training. The functional connectivity corresponding to different fatigue degrees during whole experiment were analyzed. Meanwhile, taking the clustering coefficient (C) values, degree(D) and characteristic path length (L) values as input respectively while PERCLOS as output. The results suggest that brain network analysis approaches combined with Neural Network are effective to predicts individual mental fatigue during long time driving and can improve the performance of fatigue detection with a higher prediction correlation coefficient of 0.99 and a lower RMSE value of 0.01 on average.