TY - GEN
T1 - Observation-based calibration of brain-machine interfaces for grasping
AU - Agashe, Harshavardhan A.
AU - Contreras-Vidal, Jose L.
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Brain-machine interfaces (BMIs) are increasingly being used in rehabilitation research to improve the quality of life of clinical populations. Current BMI technology allows us to control, with a high level of accuracy, the positioning of robotic hands in space. We have shown previously that it is possible to decode the dexterous movements of fingers during grasping, from noninvasively recorded electroencephalographic (EEG) activity. Due to the absence of overt movement in clinical subjects with impaired hand function, however, it is not possible to construct decoder models directly by simultaneously recording brain activity and kinematics. The mirror neuron system is activated in a similar fashion during both overt movements and observing movements performed by other agents. Here, we investigate action-observation as a strategy to calibrate decoders for grasping in human subjects. Subjects observed while a robotic hand performed grasping movements, and decode models were calibrated using the EEG activity of the subjects and the kinematics of the robotic hand. Decoding accuracy was tested on unseen data, in an 8-fold cross validation scheme, as the correlation coefficient between the predicted and actual trajectories. High decoding accuracies were obtained (r = 0.70 ± 0.07), demonstrating the feasibility of using action-observation as a calibration technique for decoding grasping movements.
AB - Brain-machine interfaces (BMIs) are increasingly being used in rehabilitation research to improve the quality of life of clinical populations. Current BMI technology allows us to control, with a high level of accuracy, the positioning of robotic hands in space. We have shown previously that it is possible to decode the dexterous movements of fingers during grasping, from noninvasively recorded electroencephalographic (EEG) activity. Due to the absence of overt movement in clinical subjects with impaired hand function, however, it is not possible to construct decoder models directly by simultaneously recording brain activity and kinematics. The mirror neuron system is activated in a similar fashion during both overt movements and observing movements performed by other agents. Here, we investigate action-observation as a strategy to calibrate decoders for grasping in human subjects. Subjects observed while a robotic hand performed grasping movements, and decode models were calibrated using the EEG activity of the subjects and the kinematics of the robotic hand. Decoding accuracy was tested on unseen data, in an 8-fold cross validation scheme, as the correlation coefficient between the predicted and actual trajectories. High decoding accuracies were obtained (r = 0.70 ± 0.07), demonstrating the feasibility of using action-observation as a calibration technique for decoding grasping movements.
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U2 - 10.1109/NER.2013.6695856
DO - 10.1109/NER.2013.6695856
M3 - Conference contribution
AN - SCOPUS:84897693827
SN - 9781467319690
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 1
EP - 4
BT - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
T2 - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Y2 - 6 November 2013 through 8 November 2013
ER -