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Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface
Tim M Blakely1, Jared D Olson2, Kai J Miller3
1Department of Bioengineering, University of Washington, Seattle WA, 2419 8th Ave N Apt 402, Seattle, WA 98109. This author's research studies the underlying cortical organization of human sensorimotor function, specializing in multi-day micro-electrocorticographic array recordings in human subjects. This type of research is applicable in many areas of cognitive neuroscience, from brain-computer interfacing to neural engineering and robotics.
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 3 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.
