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 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.

Keywords:

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

Acknowledgements

This work was supported by the United States of America National Institutes of Health grant numbers R01 NS065186, T32 NS007144, K12 2K12HD001097; the United States of America National Science Foundation grant numbers EEC-1028725 and NSF 0930908; the United States of America Army Research Office grant number W911NF-11-1-0307; and grants from the W.M. Keck Foundation and the Washington State Life Science Discovery Fund.