Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of...

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Biomedical Engineering

Background:

• Sensorimotor rhythm (SMR) brain-computer interfaces (BCIs) offer control signals but have variable user performance (10-50% unreliable control).
• Predicting individual BCI success is essential for user benefit and advancing BCI research.
• Previous studies identified visuo-motor coordination and concentration as predictors using machine learning.

Purpose of the Study:

• To replicate and validate predictors of SMR-BCI performance.
• To assess predictor accuracy in a neurofeedback-based SMR-BCI without machine learning.
• To investigate the role of psychological variables in predicting BCI control.

Main Methods:

• Thirty-three healthy novices underwent calibration and neurofeedback training sessions.
• Visuo-motor control ability and attentional impulsivity were assessed.
• Linear regression analyzed the relationship between predictors and SMR-BCI performance.

Main Results:

• Visuo-motor control ability and attentional impulsivity were related to SMR-BCI performance.
• These psychological variables accounted for nearly 20% of the performance variance.
• A prior regression model predicted performance with a 12.07% average error, with over 50% of participants showing <10% error.

Conclusions:

• Psychological variables play a moderate role in predicting SMR-BCI performance.
• The findings support the utility of psychological assessments for BCI user selection.
• Further research is needed to confirm these predictors in neurofeedback-based BCIs.