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Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report three characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

Abstract

Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report three characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with...
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Publisher
Taylor & Francis
Copyright
© 2014 Taylor & Francis
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2014.954183
Publisher site
See Article on Publisher Site

Abstract

Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report three characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Oct 2, 2014

Keywords: brain-computer interface (BCI); brain-machine interface (BMI); motor learning; motor imagery; high gamma; electrocorticography (ECoG)

References