Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject

• Published in: Journal of neural engineering Vol. 13; no. 2; p. 26017
• Main Authors: Hotson, Guy, McMullen, David P, Fifer, Matthew S, Johannes, Matthew S, Katyal, Kapil D, Para, Matthew P, Armiger, Robert, Anderson, William S, Thakor, Nitish V, Wester, Brock A, Crone, Nathan E
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.04.2016
• Subjects: Activation; Artificial Limbs; BMI; brain-computer interface; Brain-Computer Interfaces; brain-machine interface; Classification; ECoG; electrocorticography; Electrocorticography - methods; Electrodes; Electrodes, Implanted; finger; Fingers; Fingers - physiology; Humans; Male; Modular; Movement - physiology; Movements; neural control; Online; Prosthetics; Sensorimotor Cortex - physiology; User-Computer Interface; Vibration; Young Adult
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2560/13/2/026017

Objective. We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers. Approach. Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory modular prosthetic limb. Main results. The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance. Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.