UC Irvine

OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) is a promising platform for controlling arm prostheses. To restore functional independence, a BCI must be able to control arm prostheses along at least six degrees-of-freedoms (DOFs). Prior studies suggest that standard ECoG grids may be insufficient to decode multi-DOF arm movements. This study compared the ability of standard and high-density (HD) ECoG grids to decode the presence/absence of six elementary arm movements and the type of movement performed. APPROACH: Three subjects implanted with standard grids (4 mm diameter, 10 mm spacing) and three with HD grids (2 mm diameter, 4 mm spacing) had ECoG signals recorded while performing the following movements: (1) pincer grasp/release, (2) wrist flexion/extension, (3) pronation/supination, (4) elbow flexion/extension, (5) shoulder internal/external rotation, and (6) shoulder forward flexion/extension. Data from the primary motor cortex were used to train a state decoder to detect the presence/absence of movement, and a six-class decoder to distinguish between these movements. MAIN RESULTS: The average performances of the state decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those of their standard grid counterparts across all combinations of the μ, β, low-γ, and high-γ frequency bands. The average best decoding error for HD grids was 2.6%, compared to 8.5% of standard grids (chance 50%). The movement decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those based on standard ECoG across all band combinations. The average best decoding errors of 11.9% and 33.1% were obtained for HD and standard grids, respectively (chance error 83.3%). These improvements can be attributed to higher electrode density and signal quality of HD grids. SIGNIFICANCE: Commonly used ECoG grids are inadequate for multi-DOF BCI arm prostheses. The performance gains by HD grids may eventually lead to independence-restoring BCI arm prosthesis.