TY - JOUR
T1 - Decoding dexterous finger movements in a neural prosthesis model approaching real-world conditions
AU - Egan, Joshua
AU - Baker, Justin
AU - House, Paul A.
AU - Greger, Bradley
N1 - Funding Information:
Manuscript received August 22, 2011; revised March 27, 2012; accepted June 17, 2012. Date of publication August 03, 2012; date of current version November 02, 2012. This work was supported in part by DARPA Revolutionizing Prosthetics 2009.
PY - 2012
Y1 - 2012
N2 - Dexterous finger movements can be decoded from neuronal action potentials acquired from a nonhuman primate using a chronically implanted Utah Electrode Array. We have developed an algorithm that can, after training, detect and classify individual and combined finger movements without any a priori knowledge of the data, task, or behavior. The algorithm is based on changes in the firing rates of individual neurons that are tuned for one or more finger movement types. Nine different movement types, which consisted of individual flexions, individual extensions, and combined flexions of the thumb, index finger, and middle finger, were decoded. The algorithm performed reliably on data recorded continuously during movement tasks, including a no-movement state, with an overall average sensitivity and specificity that were both >92%. These results demonstrate a viable algorithm for decoding dexterous finger movements under conditions similar to those required for a real-world neural prosthetic application.
AB - Dexterous finger movements can be decoded from neuronal action potentials acquired from a nonhuman primate using a chronically implanted Utah Electrode Array. We have developed an algorithm that can, after training, detect and classify individual and combined finger movements without any a priori knowledge of the data, task, or behavior. The algorithm is based on changes in the firing rates of individual neurons that are tuned for one or more finger movement types. Nine different movement types, which consisted of individual flexions, individual extensions, and combined flexions of the thumb, index finger, and middle finger, were decoded. The algorithm performed reliably on data recorded continuously during movement tasks, including a no-movement state, with an overall average sensitivity and specificity that were both >92%. These results demonstrate a viable algorithm for decoding dexterous finger movements under conditions similar to those required for a real-world neural prosthetic application.
KW - Action potential decode
KW - brain-computer interface (BCI)
KW - microelectrode array
KW - nonhuman primate
KW - primary motor cortex
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U2 - 10.1109/TNSRE.2012.2210910
DO - 10.1109/TNSRE.2012.2210910
M3 - Article
C2 - 22875261
AN - SCOPUS:84869437990
SN - 1534-4320
VL - 20
SP - 836
EP - 844
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 6
M1 - 6259887
ER -