TY - GEN
T1 - Predicting hand forces from scalp electroencephalography during isometric force production and object grasping
AU - Paek, Andrew Y.
AU - Gailey, Alycia
AU - Parikh, Pranav
AU - Santello, Marco
AU - Contreras-Vidal, Jose
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - In this study, we demonstrate the feasibility of predicting hand forces from brain activity recorded with scalp electroencephalography (EEG). Ten able-bodied subjects participated in two tasks: an isometric force production task and a grasp-and-lift task using unconstrained and constrained grasps. We found that EEG electrodes spanning central areas of the scalp were highly correlated to force rate trajectories. Moreover, EEG grand averages in central sites resembled force rate trajectories as opposed to force trajectories. The grasp-and-lift task resulted in higher decoding accuracies than the isometric force production task: across nine subjects, median accuracies for the isometric force production task were r=0.35 whereas median accuracies for unconstrained grasping were r=0.51 and for constrained grasping were r=0.50. Such results could lead to an understanding of the neural representation behind the control of hand forces and could be implemented in the neural control of closed-loop hand-based neuroprostheses.
AB - In this study, we demonstrate the feasibility of predicting hand forces from brain activity recorded with scalp electroencephalography (EEG). Ten able-bodied subjects participated in two tasks: an isometric force production task and a grasp-and-lift task using unconstrained and constrained grasps. We found that EEG electrodes spanning central areas of the scalp were highly correlated to force rate trajectories. Moreover, EEG grand averages in central sites resembled force rate trajectories as opposed to force trajectories. The grasp-and-lift task resulted in higher decoding accuracies than the isometric force production task: across nine subjects, median accuracies for the isometric force production task were r=0.35 whereas median accuracies for unconstrained grasping were r=0.51 and for constrained grasping were r=0.50. Such results could lead to an understanding of the neural representation behind the control of hand forces and could be implemented in the neural control of closed-loop hand-based neuroprostheses.
UR - http://www.scopus.com/inward/record.url?scp=84953301107&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2015.7320144
DO - 10.1109/EMBC.2015.7320144
M3 - Conference contribution
C2 - 26738044
AN - SCOPUS:84953301107
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7570
EP - 7573
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -