Blakely Tim M, Olson Jared D, Miller Kai J, Rao Rajesh P N, Ojemann Jeffrey G
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.
Brain Comput Interfaces (Abingdon). 2014 Jul 1;1(3-4):147-157. doi: 10.1080/2326263X.2014.954183.
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.
人类受试者可以通过调节初级运动皮层（M1）的高伽马活动（75-200赫兹范围内的信号功率）来学习控制一维皮层脑电图（ECoG）脑机接口（BCI）。然而，在新的BCI技能习得过程中信号的稳定性和动态变化尚未得到研究。在本研究中，我们报告了BCI训练期间高伽马控制信号演变的3个特征阶段：初始阶段，任务准确性低，伽马频谱中的功率调制相应较低；随后是第二个阶段，任务准确性提高，活动与休息之间的平均功率分离增加；最后一个阶段，任务准确性高，功率分离稳定（或降低），试验间方差减小。这些发现可能对BCI控制算法的设计和实施具有启示意义。
