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Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific
Tianjun Wang1,2, Yun-Hsuan Chen1,3, Mohamad Sawan1,3
1Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310030, China.
Bioengineering (Basel, Switzerland)
|March 29, 2023
Summary
Guided motor imagery (GMI) shows brain patterns similar to motor execution (ME), unlike standard motor imagery (MI). Microstate analysis suggests GMI may enhance neurorehabilitation BCI systems by improving brain dynamics.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Motor imagery-based brain-computer interfaces (BCI) are promising for rehabilitation.
- Visually guided motor imagery (GMI) aims to boost rehabilitation impact, but results are mixed.
- Electroencephalography (EEG) signals can be analyzed using microstates to understand brain dynamics.
Purpose of the Study:
- To explore dynamic brain activation patterns during motor imagery (MI), motor execution (ME), and guided MI (GMI) using EEG microstate and functional connectivity analyses.
- To identify potential biomarkers for motor conditions from microstate parameters.
- To evaluate the effectiveness of microstate parameters in classifying different motor tasks using a support vector machine (SVM).
Main Methods:
- EEG data acquisition during MI, ME, and GMI tasks.
- EEG microstate analysis to compare brain topography parameters across conditions.
- Microstate-specific functional connectivity analysis using graph theory.
- SVM classification using microstate parameters for task discrimination.
Main Results:
- GMI exhibited brain activation patterns more similar to ME than MI, based on microstate analysis.
- Mean duration and duration of microstate four were identified as potential biomarkers for motor conditions.
- SVM achieved 80.27% accuracy for ME vs. MI and 66.30% for GMI classification.
- Functional connectivity analysis revealed strong relationships with microstates and dynamic switching of key brain network nodes.
Conclusions:
- Microstate analysis suggests GMI shares brain dynamics with ME.
- Functional connectivity analysis indicates visual guidance in GMI might decrease brain network integration during MI.
- Combining MI and GMI in BCI systems could enhance neurorehabilitation outcomes.
- Findings offer insights into microstate mechanisms, visual guidance in MI, and BCI-aided rehabilitation development.
