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Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.

Yujie Cui1, Songyun Xie1, Yingxin Fu1,2

  • 1Shaanxi Joint International Research Center on Integrated Technique of Brain-Computer for Unmanned System, Northwestern Polytechnical University, Xi'an 710129, China.

Brain Sciences
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a resting-state electroencephalography (EEG) microstate predictor to improve brain-computer interface (BCI) performance. This predictor accurately identifies subjects likely to perform well in motor imagery (MI) BCI tasks, enhancing subject selection and BCI development.

Keywords:
microstate analysismotor imagerysubjects’ MI-BCI performance

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Brain-Computer Interface (BCI)

Background:

  • Motor imagery (MI) electroencephalography (EEG) is a key technology for brain-computer interfaces (BCIs).
  • Significant variations in inter-subject MI-BCI performance pose a challenge for widespread application.
  • EEG microstates offer high spatiotemporal resolution and multichannel information to represent brain cognitive function.

Purpose of the Study:

  • To investigate the relationship between resting-state EEG microstate features and individual differences in MI-BCI performance.
  • To develop and validate a predictor for MI-BCI performance based on resting-state EEG microstates.

Main Methods:

  • Calculated four EEG microstate feature parameters: mean duration, occurrences per second, time coverage ratio, and transition probability.
  • Assessed the correlation between these resting-state microstate features and subjects' MI-BCI performance.
  • Proposed a resting-state microstate predictor based on identified correlations (negative for MS1 occurrence, positive for MS3 mean duration).

Main Results:

  • The proposed resting-state microstate predictor achieved an average area under the curve (AUC) of 0.83 in experiments with 28 subjects.
  • The microstate predictor demonstrated a 17.9% improvement in AUC compared to the spectral entropy predictor.
  • Higher AUC values for the microstate predictor were observed at both single-session and average levels.

Conclusions:

  • Resting-state EEG microstate features are effective predictors of MI-BCI performance.
  • The developed microstate predictor significantly outperforms the spectral entropy predictor in assessing MI-BCI performance.
  • This predictor can aid researchers in subject selection, save time, and accelerate the development of MI-BCI technology.