Medial wall contributions to finger motor decoding from electrocorticography

Area of Science:

• Neuroscience
• Biomedical Engineering
• Brain-Computer Interfaces

Background:

• Future motor brain-computer interfaces (BCIs) require integrating neural signals from diverse motor-related brain regions.
• The medial wall of the cerebral hemisphere is an underexplored region for motor decoding compared to the lateral sensorimotor cortex.

Purpose of the Study:

• To investigate the contribution of medial wall brain regions to decoding finger movements.
• To explore the potential of medial wall electrocorticography (ECoG) signals for invasive motor BCI applications.

Main Methods:

• Analyzed ECoG data from four subjects performing finger movements.
• Applied single- and multi-channel decoding to medial wall electrodes, using time- and frequency-domain features (local motor potentials, alpha/beta band oscillations).
• Assessed decoding performance for movement detection and finger discrimination.

Main Results:

• Significantly above-chance finger movement detection was achieved across multiple medial wall subregions.
• Local motor potentials and alpha-beta band power were key features for decoding, showing similarities and region-specific patterns compared to primary motor cortex.
• Medial wall channels enabled individual finger discrimination in some subjects and preliminary decoding of ipsilateral/contralateral movements.

Conclusions:

• The medial wall is identified as a viable source of motor-related signals for finger movement decoding.
• Findings support the potential of medial wall ECoG for future invasive motor BCI applications.
• Further research is needed to confirm the generalizability of these findings across individuals.