EEG-based classification of imaginary left and right foot movements using beta rebound

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Cortical lateralization of motor imagery is crucial for understanding brain function.
• Brain-computer interfaces (BCIs) offer novel avenues for human-computer interaction and assistive technologies.

Purpose of the Study:

• To investigate cortical lateralization during left and right foot motor imagery.
• To assess the classification accuracy of these imagined movements in a BCI.

Main Methods:

• Recorded 31-channel electroencephalograms (EEGs) from nine healthy subjects during foot motor imagery tasks.
• Analyzed EEG data using time-frequency maps and topographies.
• Calculated classification accuracy between left and right foot movements.

Main Results:

• Identified beta rebound in EEGs, enabling discrimination between left and right foot imagery.
• Achieved high classification accuracy (up to 81.6%) in single-trial analysis.
• Demonstrated left-right differences in EEG during foot motor imagery.

Conclusions:

• Foot motor imagery elicits distinct EEG patterns, indicating potential for cortical lateralization.
• Unilateral foot imagery-based BCIs can achieve high classification accuracy, comparable to hand-based BCIs.
• This novel BCI system could be used to control foot neuroprostheses or robotic devices.