Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNSRE.2013.2286955
Title: Simultaneous neural control of simple reaching and grasping with the modular prosthetic limb using intracranial EEG
Authors: Fifer, M.S.
Hotson, G.
Wester, B.A.
McMullen, D.P.
Wang, Y.
Johannes, M.S.
Katyal, K.D.
Helder, J.B.
Para, M.P.
Vogelstein, R.J.
Anderson, W.S.
Thakor, N.V. 
Crone, N.E.
Keywords: Brain-machine interface (BMI)
electrocorticography
functional mapping
high gamma
upper limb prosthesis
Issue Date: 2014
Citation: Fifer, M.S., Hotson, G., Wester, B.A., McMullen, D.P., Wang, Y., Johannes, M.S., Katyal, K.D., Helder, J.B., Para, M.P., Vogelstein, R.J., Anderson, W.S., Thakor, N.V., Crone, N.E. (2014). Simultaneous neural control of simple reaching and grasping with the modular prosthetic limb using intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering 22 (3) : 695-705. ScholarBank@NUS Repository. https://doi.org/10.1109/TNSRE.2013.2286955
Abstract: Intracranial electroencephalographic (iEEG) signals from two human subjects were used to achieve simultaneous neural control of reaching and grasping movements with the Johns Hopkins University Applied Physics Lab (JHU/APL) Modular Prosthetic Limb (MPL), a dexterous robotic prosthetic arm. We performed functional mapping of high gamma activity while the subject made reaching and grasping movements to identify task-selective electrodes. Independent, online control of reaching and grasping was then achieved using high gamma activity from a small subset of electrodes with a model trained on short blocks of reaching and grasping with no further adaptation. Classification accuracy did not decline (p < 0.05, one-way ANOVA) over three blocks of testing in either subject. Mean classification accuracy during independently executed overt reach and grasp movements for (Subject 1, Subject 2) were (0.85, 0.81) and (0.80, 0.96), respectively, and during simultaneous execution they were (0.83, 0.88) and (0.58, 0.88), respectively. Our models leveraged knowledge of the subject's individual functional neuroanatomy for reaching and grasping movements, allowing rapid acquisition of control in a time-sensitive clinical setting. We demonstrate the potential feasibility of verifying functionally meaningful iEEG-based control of the MPL prior to chronic implantation, during which additional capabilities of the MPL might be exploited with further training. © 2014 IEEE.
Source Title: IEEE Transactions on Neural Systems and Rehabilitation Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/128670
ISSN: 15344320
DOI: 10.1109/TNSRE.2013.2286955
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