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Related Experiment Video

Updated: Oct 9, 2025

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Exploring Fatigue Effects on Performance Variation of Intensive Brain-Computer Interface Practice.

Songwei Li1, Junyi Duan1, Yu Sun2

  • 1State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China.

Frontiers in Neuroscience
|December 20, 2021
PubMed
Summary
This summary is machine-generated.

Intensive motor imagery (MI) brain-computer interface (BCI) practice can alter mental states like fatigue. However, BCI performance remains stable despite these changes, indicating resilience in the system.

Keywords:
brain–computer interfaceelectroencephalogram (EEG)fatiguemotor imageryrest condition

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Motor imagery (MI) is a key electroencephalogram (EEG)-based brain-computer interface (BCI) strategy.
  • Previous research indicates mental fatigue negatively impacts non-feedback BCI performance.
  • The effect of fatigue on online, feedback-enabled MI-BCI remains unclear.

Purpose of the Study:

  • To investigate the impact of fatigue on online MI-BCI performance.
  • To assess changes in mental state and EEG signatures during prolonged MI-BCI use.
  • To explore the influence of different rest conditions on BCI outcomes.

Main Methods:

  • 12 healthy subjects participated in an MI-BCI experiment over four sessions.
  • Sessions included training and three different rest conditions: no rest, 16-min eyes-open, and 16-min eyes-closed rest.
  • Mental states were assessed using the Multidimensional Fatigue Inventory (MFI) and Short Stress State Questionnaire (SSSQ).
  • BCI performance metrics (PVC, ITR) and EEG data (alpha-band power, ERD) were analyzed.

Main Results:

  • Significant increases in general fatigue, mental fatigue, and distress were observed, alongside decreased engagement in certain sessions.
  • Despite mental state changes, BCI performance (PVC, ITR) showed no significant decline across trials.
  • Electrophysiological analysis indicated increased alpha-band power and decreased ERD modulation, with distinct patterns observed during eyes-closed rest.

Conclusions:

  • Intensive MI-BCI practice can induce significant changes in users' mental states.
  • Online MI-BCI performance demonstrates robustness and is not significantly affected by task-induced fatigue within a session.
  • Rest conditions, particularly eyes-closed rest, influence EEG dynamics but do not impair BCI functionality.