McFarland Dennis J, Krusienski Dean 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.
Prog Brain Res. 2006;159:411-9. doi: 10.1016/S0079-6123(06)59026-0.
The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.
基于μ和β感觉运动节律的沃兹沃思脑机接口（BCI），采用一维和二维光标移动任务，并依赖用户训练。这是一个实时闭环系统。信号处理包括通道选择、空间滤波和频谱分析。特征转换采用回归方法和归一化。在此过程中，会根据不同的标准和方法在多个点进行自适应调整。它既可以使用前馈（例如，估计信号均值进行归一化），也可以使用反馈控制（例如，估计预测方程的特征权重）。我们将这个过程视为动态用户与动态系统之间随时间共同适应的交互作用。理解这种交互作用的动态特性并优化其性能是BCI研究的一项重大挑战。
