Observation-based calibration of brain-machine interfaces for grasping

Harshavardhan A. Agashe, Jose L. Contreras Vidal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages1-4
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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