Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials
(2024) In Journal of Neuroscience Methods 406.- Abstract
Background: 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... (More)
Background: 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.
(Less)
- author
- Liu, Jia LU ; Zhu, Yongjie ; Cong, Fengyu ; Björkman, Anders LU ; Malesevic, Nebojsa LU and Antfolk, Christian LU
- organization
- publishing date
- 2024-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Event-related potentials (ERPs), Intra-individual variability (IIV), Mental fatigue, Residue iteration decomposition (RIDE), Single-trial analysis, Temporal principal component analysis (PCA)
- in
- Journal of Neuroscience Methods
- volume
- 406
- article number
- 110110
- publisher
- Elsevier
- external identifiers
-
- pmid:38499275
- scopus:85188791845
- ISSN
- 0165-0270
- DOI
- 10.1016/j.jneumeth.2024.110110
- language
- English
- LU publication?
- yes
- id
- 6f430cef-d2df-41bc-b06b-3e3c9a6e2af4
- date added to LUP
- 2024-04-15 09:31:30
- date last changed
- 2024-04-15 09:31:59
@article{6f430cef-d2df-41bc-b06b-3e3c9a6e2af4,
abstract = {{<p>Background: 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.</p>}},
author = {{Liu, Jia and Zhu, Yongjie and Cong, Fengyu and Björkman, Anders and Malesevic, Nebojsa and Antfolk, Christian}},
issn = {{0165-0270}},
keywords = {{Event-related potentials (ERPs); Intra-individual variability (IIV); Mental fatigue; Residue iteration decomposition (RIDE); Single-trial analysis; Temporal principal component analysis (PCA)}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Journal of Neuroscience Methods}},
title = {{Analysis of modulations of mental fatigue on intra-individual variability from single-trial event related potentials}},
url = {{http://dx.doi.org/10.1016/j.jneumeth.2024.110110}},
doi = {{10.1016/j.jneumeth.2024.110110}},
volume = {{406}},
year = {{2024}},
}