Quantifying the role of motor imagery in brain-machine interfaces

Area of Science:

• Neuroscience
• Biomedical Engineering
• Human-Computer Interaction

Background:

• Brain-machine interfaces (BMIs) offer potential for individuals with motor impairments.
• Control of BMIs, particularly those using motor imagery (MI), is inconsistent across users.
• The reasons for varying BMI control aptitude remain largely unknown.

Purpose of the Study:

• To investigate the relationship between motor imagery (MI) ability and brain-machine interface (BMI) control proficiency.
• To identify specific MI characteristics that differentiate high-aptitude BMI users from low-aptitude users.
• To explore the utility of MI assessments as predictors of BMI performance.

Main Methods:

• Assessed kinesthetic and visual motor imagery (MI) abilities.
• Measured behavioral accuracy of MI.
• Analyzed electroencephalographic (EEG) variables, including lateralized μ-band oscillations.
• Applied mental chronometry to compare temporal profiles of imagined and executed movements.

Main Results:

• High-aptitude BMI users demonstrated significantly higher MI accuracy via subjective and behavioral measures.
• Kinesthetic imagery appeared more critical for BMI control than visual imagery.
• Enhanced lateralized μ-band oscillations over sensorimotor cortices were observed in high-aptitude users during MI.
• Mental chronometry revealed distinct temporal profiles between high and low-aptitude users.

Conclusions:

• Subjective, behavioral, and EEG measures of MI are strongly correlated with BMI control performance.
• Individual MI ability, particularly kinesthetic, is a key determinant of BMI success.
• Questionnaires and mental chronometry can predict BMI performance, potentially reducing reliance on EEG recordings.