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Decoding three-dimensional reaching movements using electrocorticographic signals in humans.

David T Bundy1, Mrinal Pahwa, Nicholas Szrama

  • 1Department of Biomedical Engineering, Washington University in St. Louis, Campus Box 8057, 660 South Euclid, St Louis, MO 63130, USA.

Journal of Neural Engineering
|February 24, 2016
PubMed
Summary

Brain-computer interfaces (BCIs) can decode 3D arm movements using electrocorticography (ECoG) signals. This study shows ECoG can predict hand speed, velocity, and position, advancing BCI capabilities.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Engineering

Background:

  • Electrocorticography (ECoG) offers a balance between signal quality and invasiveness for brain-computer interface (BCI) applications.
  • Previous BCI research using ECoG has primarily focused on decoding motor movements, with the full extent of decodable information remaining uncertain.

Purpose of the Study:

  • To determine if ECoG signals can be used to decode the kinematics (speed, velocity, and position) of arm movements in three-dimensional (3D) space.
  • To assess the potential of ECoG for developing advanced, multi-degree-of-freedom BCI systems in human patients.

Main Methods:

  • A 3D center-out reaching task was designed and performed by five epileptic patients with temporary ECoG array placement.
  • A hierarchical partial-least squares (PLS) regression model was employed to predict hand kinematics from ECoG signals in an offline analysis.

Main Results:

  • The hierarchical PLS model successfully predicted hand speed, velocity, and position during 3D reaching movements with accuracies significantly above chance across all patients.
  • Correlation coefficients for predicted vs. actual kinematics ranged from 0.31-0.80 for speed, 0.27-0.54 for velocity, and 0.22-0.57 for position.
  • While beta band power changes were key for movement/rest classification, local motor potential and high gamma band power changes were most influential in predicting kinematic parameters.

Conclusions:

  • This study provides the first evidence that fully three-dimensional arm movements can be predicted from ECoG recordings in human patients.
  • The findings highlight the significant potential of ECoG for creating sophisticated BCI systems with multiple degrees of freedom for clinical applications.