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EEG Spectral Feature Modulations Associated With Fatigue in Robot-Mediated Upper Limb Gross and Fine Motor
Udeshika C Dissanayake1, Volker Steuber1, Farshid Amirabdollahian1
1School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
Frontiers in Neurorobotics
|February 7, 2022
Summary
Robot-assisted upper limb exercises induce fatigue, altering brain activity patterns. EEG spectral features, like alpha and delta band power, change, indicating physical and mental fatigue, potentially aiding post-stroke therapy detection.
Area of Science:
- Neuroscience
- Rehabilitation Engineering
- Human-Computer Interaction
Background:
- Robot-mediated therapies are increasingly used in upper limb rehabilitation.
- Understanding fatigue during these interactions is crucial for optimizing treatment.
- Electroencephalography (EEG) offers a non-invasive method to assess brain activity changes related to fatigue.
Purpose of the Study:
- To investigate EEG spectral feature modulations during robot-mediated upper limb gross and fine motor fatigue.
- To differentiate fatigue patterns induced by gross versus fine motor tasks.
- To explore the potential application of EEG in detecting and moderating fatigue in post-stroke robotic therapies.
Main Methods:
- Twenty healthy participants performed robot-mediated gross (HapticMASTER) or fine (SCRIPT) motor tasks until volitional fatigue.
- EEG data were recorded before and after the tasks.
- Relative and ratio band power measures (alpha, delta, theta, beta) were analyzed using paired-samples t-tests.
Main Results:
- Fatigue induced by both gross and fine motor tasks significantly increased relative alpha band power and decreased relative delta band power.
- Gross motor tasks increased (θ + α)/β and α/β ratios, while fine motor tasks increased relative theta band power.
- Gross movements altered EEG activity in central and parietal regions; fine movements affected frontopolar and central regions, suggesting task-specific fatigue (physical vs. mental).
Conclusions:
- Localized changes in EEG spectral features reliably indicate fatigue from robot-mediated interactions.
- Differences in EEG patterns between gross and fine motor tasks correlate with distinct physical and mental fatigue levels.
- These findings support the use of EEG for fatigue monitoring and management in robot-assisted post-stroke rehabilitation.
