Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals

Harshavardhan A. Agashe, José L. Contreras-Vidal

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

34 Scopus citations

Abstract

With continued research on brain machine interfaces (BMIs), it is now possible to control prosthetic arm position in space to a high degree of accuracy. However, a reliable decoder to infer the dexterous movements of fingers from brain activity during a natural grasping motion is still to be demonstrated. Here, we present a methodology to accurately predict and reconstruct natural hand kinematics from non-invasively recorded scalp electroencephalographic (EEG) signals during object grasping movements. The high performance of our decoder is attributed to a combination of the correct input space (time-domain amplitude modulation of delta-band smoothed EEG signals) and an optimal subset of EEG electrodes selected using a genetic algorithm. Trajectories of the joint angles were reconstructed for metacarpo-phalangeal (MCP) joints of the fingers as well as the carpo-metacarpal (CMC) and MCP joints of the thumb. High decoding accuracy (Pearson's correlation coefficient, r) between the predicted and observed trajectories (r 0.760.01; averaged across joints) indicate that this technique may be suitable for use with a closed-loop real-time BMI to control grasping motion in prosthetics with high degrees of freedom. This demonstrates the first successful decoding of hand pre-shaping kinematics from noninvasive neural signals.

Original languageEnglish
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Pages5444-5447
Number of pages4
Volume2011
DOIs
StatePublished - Dec 26 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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