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Brain Comput Interfaces (Abingdon). 2014 Jul 1;1(3-4):147-157.

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

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Department 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.
Department of Rehabilitation Medicine, University of Washington, Seattle, WA. Center for Sensorimotor Neural Engineering, Seattle, WA, Box 359740, 325 9 Avenue, Seattle, WA 98104, 206-744-5862, The author researches human sensorimotor neurophysiology, clinical translation of brain-machine interfaces, and is a physiatrist (physical medicine and rehabilitation physician) specializing in neurorehabilitation.
Prior Affiliation: Department of Neurobiology and Behavior, University of Washington, Seattle, WA. Current Affiliation: Department of Neurological Surgery, Stanford School of Medicine, Stanford, CA, 300 Pasteur Drive, Stanford, CA 94305-5327, Kai is interested in distributed brain dynamics and systems neuroscience, and is currently in a neurosurgery residency.
Center for Sensorimotor Neural Engineering, Seattle, WA. Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195, (206) 685-9141. The author's research spans the areas of brain-computer interfacing, computational neuroscience and robotics.
Neurological Surgery and Radiology, University of Washington, Center for Sensorimotor Neural Engineering, Seattle, WA, Center for Integrative Brain Research, Seattle Children's Research Institute, Box 359300, Seattle, WA 98195, (206) 987-4240. This author's research is electrocorticographic studies of cognitive function and brain-machine interface and is a neurosurgeon specializing in the treatment of pediatric and epilepsy neurosurgery.


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.


Brain-Computer Interface (BCI); Brain-Machine Interface (BMI); electrocorticography (ECoG); high gamma; motor imagery; motor learning

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