Personalized μ-transcranial alternating current stimulation improves online brain-computer interface control

Area of Science:

• Neuroscience
• Biomedical Engineering

Background:

• Motor imagery (MI) based brain-computer interfaces (BCIs) decode electroencephalography (EEG) signals for limb movement control.
• Many users struggle with BCI proficiency due to unreliable MI patterns, limiting system performance.

Purpose of the Study:

• To enhance online MI-BCI performance using a personalized, biomarker-driven approach with transcranial alternating current stimulation (tACS).
• To investigate the neural correlates of MI-BCI performance and the effects of tACS on sensorimotor rhythms.

Main Methods:

• Applied 20-minute tACS at participant-specific peak µ frequency in resting-state sensorimotor rhythms (SMRs).
• Utilized an active-controlled, single-blind study design with healthy participants (N=10) and a control group (N=10).

Main Results:

• Observed significant improvements in µ SMR event-related desynchronizations (ERDs) after tACS.
• Demonstrated enhanced online MI-BCI performance in decoding left vs. right hand commands in the tACS group.
• Found a significant correlation between resting-state µ SMRs and µ ERDs, explaining BCI performance changes.

Conclusions:

• tACS shows promise as a non-invasive intervention to improve BCI performance.
• This approach can enhance the independence and interaction capabilities of individuals relying on BCI systems.