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Motor imagery and mental fatigue: inter-relationship and EEG based estimation
Upasana Talukdar1, Shyamanta M Hazarika2, John Q Gan3
1Biomimetic and Cognitive Robotics Lab, Department of Computer Science and Engineering, Tezpur University, Tezpur, India. upat123@tezu.ernet.in.
Journal of Computational Neuroscience
|November 30, 2018
Summary
Motor imagery (MI) significantly induces mental fatigue. This fatigue then degrades the performance of brain-computer interfaces (BCI) by reducing EEG signal separability, highlighting the need for adaptive BCI systems.
Area of Science:
- Neuroscience
- Human-Computer Interaction
- Biomedical Engineering
Background:
- Psychological states are hypothesized to influence brain-computer interface (BCI) performance.
- Formal analysis linking motor imagery (MI) and mental fatigue in BCI is limited.
Purpose of the Study:
- To investigate the relationship between prolonged motor imagery (MI) and mental fatigue.
- To determine if mental fatigue impacts the electroencephalography (EEG) class separability of MI.
Main Methods:
- Utilized electroencephalography (EEG) to monitor participants during a motor imagery (MI) task.
- Employed the Kernel Partial Least Square (KPLS) algorithm to track the progression of mental fatigue.
- Conducted statistical analysis to assess the effect of fatigue on MI EEG class separability.
Main Results:
- Prolonged MI tasks were found to induce substantial mental fatigue in participants.
- High levels of mental fatigue significantly decreased the class separability of MI-related EEG signals.
- A notable portion of participants (5 out of 11) withdrew due to excessive fatigue.
Conclusions:
- Established a clear inter-connection between motor imagery (MI) and mental fatigue.
- Demonstrated that mental fatigue negatively impacts BCI performance by reducing EEG signal discriminability.
- Emphasized the critical need for adaptive BCI systems that monitor and account for mental fatigue.
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