Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas.
OBJECTIVE
Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.


METHODS
We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants.


RESULTS
Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system.


CONCLUSIONS
These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and characterization of non-stationarities such that ECoG could be a viable signal source for grasp control for amputees or individuals with paralysis.
Stanford Institute for Neuroinnovation and Translational Neuroscience, W100-A, James H Clark Center, Stanford University, Stanford, CA 94305-5436, USA.
Cynthia A
CA
Chestek
Vikash
V
Gilja
Christine H
CH
Blabe
Brett L
BL
Foster
Krishna V
KV
Shenoy
Josef
J
Parvizi
Jaimie M
JM
Henderson
D001132:Arm / Q000502:physiology; D003199:Computer Systems; D004566:Electrodes; D004569:Electroencephalography; D005385:Fingers / Q000502:physiology; D006225:Hand / Q000502:physiology*; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology; D009862:Online Systems; D011187:Posture / Q000502:physiology*; D019736:Prostheses and Implants; D011474:Prosthesis Design; D015203:Reproducibility of Results; D012146:Rest; D012640:Seizures / Q000175:diagnosis; D013003:Somatosensory Cortex / Q000502:physiology*; D014584:User-Computer Interface