Continuous decoding of human grasp kinematics using epidural and subdural signals

Objective. Restoring or replacing function in paralyzed individuals will one day be achieved through the use of brain-machine interfaces. Regaining hand function is a major goal for paralyzed patients. Two competing prerequisites for the widespread adoption of any hand neuroprosthesis are accurate control over the fine details of movement, and minimized invasiveness. Here, we explore the interplay between these two goals by comparing our ability to decode hand movements with subdural and epidural field potentials (EFPs). Approach. We measured the accuracy of decoding continuous hand and finger kinematics during naturalistic grasping motions in five human subjects. We recorded subdural surface potentials (electrocorticography; ECoG) as well as with EFPs, with both standard- and high-resolution electrode arrays. Main results. In all five subjects, decoding of continuous kinematics significantly exceeded chance, using either EGoG or EFPs. ECoG decoding accuracy compared favorably with prior investigations of grasp kinematics (mean ± SD grasp aperture variance accounted for was 0.54 ±0.05 across all subjects, 0.75 ±0.09 for the best subject). In general, EFP decoding performed comparably to ECoG decoding. The 7-20 Hz and 70-115 Hz spectral bands contained the most information about grasp kinematics, with the 70-115 Hz band containing greater information about more subtle movements. Higher-resolution recording arrays provided clearly superior performance compared to standard-resolution arrays. Significance. To approach the fine motor control achieved by an intact brain-body system, it will be necessary to execute motor intent on a continuous basis with high accuracy. The current results demonstrate that this level of accuracy might be achievable not just with ECoG, but with EFPs as well. Epidural placement of electrodes is less invasive, and therefore may incur less risk of encephalitis or stroke than subdural placement of electrodes. Accurately decoding motor commands at the epidural level may be an important step towards a clinically viable brain-machine interface.