Harvy, Jonathan and Bezerianos, Anastasios and Li, Junhua (2022) Reliability of EEG Measures in Driving Fatigue. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30. pp. 2743-2753. DOI https://doi.org/10.1109/TNSRE.2022.3208374
Harvy, Jonathan and Bezerianos, Anastasios and Li, Junhua (2022) Reliability of EEG Measures in Driving Fatigue. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30. pp. 2743-2753. DOI https://doi.org/10.1109/TNSRE.2022.3208374
Harvy, Jonathan and Bezerianos, Anastasios and Li, Junhua (2022) Reliability of EEG Measures in Driving Fatigue. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30. pp. 2743-2753. DOI https://doi.org/10.1109/TNSRE.2022.3208374
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Driving fatigue; EEG; brain network; functional connectivity; graph metrics; sensor and source levels |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 20 Jan 2023 12:58 |
Last Modified: | 30 Oct 2024 15:51 |
URI: | http://repository.essex.ac.uk/id/eprint/33498 |
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