Medial Wall's Potential in Enhancing Finger Movement Decoding from Electrocorticography (ECoG): A Single-Subject...

The next generation of motor brain-computer interfaces (BCIs) will likely benefit from integrating recordings from multiple motor-related brain regions. Among these is the medial wall, yet it remains relatively understudied in the case of finger movement decoding. Using electrocorticographic (ECoG) recordings from a subject implanted both over medial and lateral cortical areas, we first assessed the medial wall's potential for multiclass classification (5 fingers + rest). We achieved a six-class accuracy of 0.46, significantly above chance, with rest trials classified most accurately, followed by thumb movement trials. Several frequency features contributed to decoding, with Local Motor Potentials (LMP) being the most influential one, with distinctive activity already prior to movement onset, and power in the α (8-12 Hz) band aiding in decoding rest trials over finger movement trials. Next, we explored whether combining the best medial wall channel with lateral cortical channels could improve decoding performance. We found a significant accuracy improvement for most lateral channels (from an average of 0.36 to 0.42), except for the channel closest to the finger primary motor region, whose accuracy was already high (0.77). These findings highlight the medial wall's potential for motor decoding and its value as a target region for future motor BCIs, especially for individuals with impaired hand motor areas.