Reliability of EEG Measures in Driving Fatigue

Reliability investigation of measures is important in studies of brain science and neuroengineering. Measures' reliability hasn't been investigated across brain states, leaving unknown how reliable the measures are in the context of the change from alert state to fatigue state during driving. To compensate for the lack, we performed a comprehensive investigation. A two-session experiment with an interval of approximately one week was designed to evaluate the reliability of the measures at both sensor and source levels. The results showed that the average intraclass correlation coefficients (ICCs) of the measures at the sensor level were generally higher than those at the source level, except for the directed between-region measures. Single-region measures generally exhibited higher average ICCs relative to between-region measures. The exploration of brain network topology showed that nodal metrics displayed highly varying ICCs across regions and global metrics varied associated with nodal metrics. Single-region measures displayed higher ICCs in the frontal and occipital regions while the between-region measures exhibited higher ICCs in the area involving frontal, central and occipital regions. This study provides an appraisal for the measures' reliability over a long interval, which is informative for measure selection in practical mental monitoring.

Automated summary

Reliability investigation of measures is crucial in brain science and neuroengineering studies. This study evaluated the reliability of measures at both sensor and source levels, finding that measures at the sensor level generally showed higher reliability than those at the source level, except for directed between-region measures. Single-region measures displayed higher reliability compared to between-region measures. Exploration of brain network topology revealed varying degrees of reliability for nodal metrics across different regions, and global metrics varied in association with nodal metrics. This study provides important insights for measure selection in practical mental monitoring.