Format

Send to

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

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

Author information

1
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.
2
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.
3
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.
4
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.
5
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.

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.

KEYWORDS:

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

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center