A1 Journal article (refereed)
Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials (2024)
Liu, J., Zhu, Y., Cong, F., Björkman, A., Malesevic, N., & Antfolk, C. (2024). Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials. Journal of Neuroscience Methods, 406, Article 110110. https://doi.org/10.1016/j.jneumeth.2024.110110
JYU authors or editors
Publication details
All authors or editors: Liu, Jia; Zhu, Yongjie; Cong, Fengyu; Björkman, Anders; Malesevic, Nebojsa; Antfolk, Christian
Journal or series: Journal of Neuroscience Methods
ISSN: 0165-0270
eISSN: 1872-678X
Publication year: 2024
Publication date: 16/03/2024
Volume: 406
Article number: 110110
Publisher: Elsevier
Publication country: Netherlands
Publication language: English
DOI: https://doi.org/10.1016/j.jneumeth.2024.110110
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/94475
Abstract
Intra-individual variability (IIV), a measure of variance within an individual’s performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging.
New methods
Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels.
Results
We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%.
Comparison with existing methods
The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue.
Conclusions
This study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.
Keywords: fatigue (biological phenomena); stress (biological phenomena); performance (capacity); exhaustion
Free keywords: mental fatigue; intra-individual variability (IIV); event-related potentials (ERPs); temporal principal component analysis (PCA); residue iteration decomposition (RIDE); single-trial analysis
Contributing organizations
Ministry reporting: Yes
VIRTA submission year: 2024
Preliminary JUFO rating: 1
