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.