Medial wall contributions to finger motor decoding from electrocorticography.

Abstract

Future motor brain-computer interfaces (BCIs) are expected to benefit from integrating neural signals from multiple motor-related brain regions. While decoding studies have largely focused on lateral sensorimotor cortex, the medial wall of the cerebral hemisphere remains relatively underexplored. Here, we investigate the contribution of medial wall regions to finger movement decoding using human electrocorticography (ECoG) recordings. We analyzed ECoG data from four subjects performing finger movements. Single- and multi-channel decoding analyses were applied to medial wall electrodes, examining the contribution of time-domain and frequency-domain features, including local motor potentials (LMP) and oscillatory power in the alpha (8-12 Hz) and beta (12-34 Hz) bands. Decoding performance was assessed for movement detection and finger discrimination. Significantly above-chance finger movement detection was observed across multiple medial wall subregions. LMP and alpha-beta band power contributed most strongly to decoding performance. Feature dynamics shared key properties with primary motor cortex, including pre-movement alpha-beta desynchronization, while also exhibiting region-specific patterns such as anatomically dependent positive or negative LMP modulations. Although movement detection was the dominant outcome, medial wall channels in two subjects enabled significant differentiation between individual fingers. In one subject, both contralateral and ipsilateral finger movements could be decoded with some generalization across hands; however, this observation is based on a single case and should be interpreted as preliminary. These findings identify the medial wall as a viable source of motor-related signals for finger movement decoding, with potential for future invasive motor BCI applications, while underscoring the need for further studies to confirm generalizability across individuals.