TY - JOUR
T1 - Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex
AU - Guan, Charles
AU - Aflalo, Tyson
AU - Kadlec, Kelly
AU - Gámez de Leon, Jorge
AU - Rosario, Emily R.
AU - Bari, Ausaf
AU - Pouratian, Nader
AU - Andersen, Richard A.
N1 - Publisher Copyright:
© 2023 The Author(s). Published by IOP Publishing Ltd.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis. Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands. Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands. Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.
AB - Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis. Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands. Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands. Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.
KW - brain-computer interface (BCI)
KW - factorized representations
KW - finger decoding
KW - hand movement
KW - motor cortex (MC)
KW - posterior parietal cortex (PPC)
KW - representational geometry
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U2 - 10.1088/1741-2552/acd3b1
DO - 10.1088/1741-2552/acd3b1
M3 - Article
C2 - 37160127
AN - SCOPUS:85160202880
SN - 1741-2560
VL - 20
JO - Journal of neural engineering
JF - Journal of neural engineering
IS - 3
M1 - 036020
ER -