McFarland Dennis J, Wolpaw Jonathan R
Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health, State University of New York, Albany, New York, USA.
Appl Psychophysiol Biofeedback. 2003 Sep;28(3):217-31. doi: 10.1023/a:1024685214655.
People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In our current EEG-based brain-computer interface (BCI) system, cursor movement is a linear function of mu or beta rhythm amplitude. In order to maximize the participant's control over the direction of cursor movement, the intercept in this equation is kept equal to the mean amplitude of recent performance. Selection of the optimal slope, or gain, which determines the magnitude of the individual cursor movements, is a more difficult problem. This study examined the relationship between gain and accuracy in a 1-dimensional EEG-based cursor movement task in which individuals select among 2 or more choices by holding the cursor at the desired choice for a fixed period of time (i.e., the dwell time). With 4 targets arranged in a vertical column on the screen, large gains favored the end targets whereas smaller gains favored the central targets. In addition, manipulating gain and dwell time within participants produces results that are in agreement with simulations based on a simple theoretical model of performance. Optimal performance occurs when correct selection of targets is uniform across position. Thus, it is desirable to remove any trend in the function relating accuracy to target position. We evaluated a controller that is designed to minimize the linear and quadratic trends in the accuracy with which participants hit the 4 targets. These results indicate that gain should be adjusted to the individual participants, and suggest that continual online gain adaptation could increase the speed and accuracy of EEG-based cursor control.
人们可以学会控制在感觉运动皮层记录的脑电图（EEG）中的μ波（8 - 12赫兹）或β波（18 - 25赫兹）节律幅度，并利用它将光标移动到视频屏幕上的目标位置。在我们当前基于脑电图的脑机接口（BCI）系统中，光标移动是μ波或β波节律幅度的线性函数。为了使参与者对光标移动方向的控制最大化，该方程中的截距保持等于近期表现的平均幅度。选择最佳斜率或增益（它决定了单个光标移动的幅度）是一个更困难的问题。本研究考察了在基于一维脑电图的光标移动任务中增益与准确性之间的关系，在该任务中，个体通过将光标在期望的选择位置保持一段固定时间（即停留时间）来在两个或更多选择中进行选择。屏幕上垂直排列有4个目标，较大的增益有利于末端目标，而较小的增益有利于中间目标。此外，在参与者内部操纵增益和停留时间产生的结果与基于简单表现理论模型的模拟结果一致。当目标位置的正确选择均匀分布时，会出现最佳表现。因此，希望消除准确性与目标位置关系函数中的任何趋势。我们评估了一种控制器，其设计目的是最小化参与者击中4个目标的准确性中的线性和二次趋势。这些结果表明，增益应根据个体参与者进行调整，并表明持续的在线增益自适应可以提高基于脑电图的光标控制的速度和准确性。
