MEAN_SP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Predicting Brain-Computer Interface (BCI) control is crucial for efficient experimental design and user training.
• Existing predictors often rely on extensive electroencephalography (EEG) data and numerous channels.
• Sensorimotor rhythm (SMR)-based BCIs are widely used, necessitating accurate performance prediction methods.

Purpose of the Study:

• To propose and validate a novel predictor for assessing an individual's potential to achieve adequate control in SMR-based BCIs.
• To evaluate the predictor's efficacy using a minimal number of EEG channels (2-5).
• To compare the novel predictor against existing state-of-the-art methods.

Main Methods:

• Development of a new predictor, denoted as [Formula: see text], for SMR-based BCI performance.
• Evaluation of the predictor on two large-scale datasets (150 and 80 participants).
• Assessment of predictor performance using 2, 3, 4, and 5 EEG channels, identifying optimal channel subsets.

Main Results:

• The proposed [Formula: see text] predictor demonstrated comparable or superior performance to existing methods.
• Optimal channel sets were identified for different channel counts, proving robust across varied experimental settings.
• The predictor was validated on large datasets, confirming its reliability and scalability.

Conclusions:

• The novel [Formula: see text] predictor offers an effective and efficient method for anticipating BCI performance.
• Its ability to function with a limited number of channels reduces data acquisition and processing requirements.
• This predictor can guide personalized training strategies and optimize BCI system development.