McFarland Dennis J, Wolpaw Jonathan R
Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health and State University of New York, Albany, NY 12201, USA.
IEEE Trans Neural Syst Rehabil Eng. 2005 Sep;13(3):372-9. doi: 10.1109/TNSRE.2005.848627.
People can learn to control electroencephalogram (EEG) features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. In the standard one-dimensional application, the cursor moves horizontally from left to right at a fixed rate while vertical cursor movement is continuously controlled by sensorimotor rhythm amplitude. The right edge of the screen is divided among 2-6 targets, and the user's goal is to control vertical cursor movement so that the cursor hits the correct target when it reaches the right edge. Up to the present, vertical cursor movement has been a linear function of amplitude in a specific frequency band [i.e., 8-12 Hz (mu) or 18-26 Hz (beta)] over left and/or right sensorimotor cortex. The present study evaluated the effect of controlling cursor movement with a weighted combination of these amplitudes in which the weights were determined by an regression algorithm on the basis of the user's past performance. Analyses of data obtained from a representative set of trained users indicated that weighted combinations of sensorimotor rhythm amplitudes could support cursor control significantly superior to that provided by a single feature. Inclusion of an interaction term further improved performance. Subsequent online testing of the regression algorithm confirmed the improved performance predicted by the offline analyses. The results demonstrate the substantial value for brain-computer interface applications of simple multivariate linear algorithms. In contrast to many classification algorithms, such linear algorithms can easily incorporate multiple signal features, can readily adapt to changes in the user's control of these features, and can accommodate additional targets without major modifications.
人们可以学会控制由感觉运动节律幅度组成的脑电图（EEG）特征，并利用这种控制在一维或二维中将光标移动到屏幕上的目标位置。在标准的一维应用中，光标以固定速率从左到右水平移动，而垂直光标移动则由感觉运动节律幅度持续控制。屏幕的右边缘被划分为2至6个目标区域，用户的目标是控制垂直光标移动，以便光标在到达右边缘时击中正确的目标区域。到目前为止，垂直光标移动一直是特定频段[即8 - 12赫兹（μ波）或18 - 26赫兹（β波）]上左和/或右感觉运动皮层幅度的线性函数。本研究评估了使用这些幅度的加权组合来控制光标移动的效果，其中权重由回归算法根据用户过去的表现来确定。对一组具有代表性的训练用户所获得的数据进行分析表明，感觉运动节律幅度的加权组合能够支持光标控制，其效果显著优于单一特征所提供的控制效果。纳入交互项进一步提高了性能。随后对回归算法进行的在线测试证实了离线分析所预测的性能提升。结果证明了简单多元线性算法在脑机接口应用中的巨大价值。与许多分类算法不同，这种线性算法可以轻松纳入多个信号特征，能够很容易地适应用户对这些特征控制的变化，并且无需进行重大修改就能适应额外的目标。
