Conversion of EEG activity into cursor movement by a brain-computer interface (BCI)
Conversion of EEG activity into cursor movement by a brain-computer interface (BCI)
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DOI：
10.1109/tnsre.2004.834627
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发表时间：
2004-09-01
影响因子：
4.9
通讯作者：
Pfurtscheller, G
中科院分区：
文献类型：
--
作者：
Fabiani, GE;McFarland, DJ;Pfurtscheller, G
基于沃兹沃斯脑电图 (EEG) 的脑机接口 (BCI) 使用感觉运动皮层上 mu 或 beta 频段的幅度来控制光标移动。经过培训的用户可以在一维或二维中移动光标。这项研究的主要目标是为患有严重运动障碍的人提供一种新的通信和控制选择。目前，每个维度的光标移动都是通过一个或两个脑电图特征（即来自不同电极位置的光谱带）的经验推导的线性函数来确定10次/秒。本研究对系统运行期间收集的数据进行离线分析，以探索提高光标移动准确性的方法。当用户通过控制垂直[即一维(1-D)]光标移动在三个可能的目标中进行选择时收集数据。所分析的三种方法的不同之处在于光标移动的维度 [1-D 与二维 (2-D)] 以及基础函数的类型（线性与非线性）。我们解决了两个问题：哪种方法最适合分类（即，从 EEG 确定用户想要击中哪个目标）？ EEG 特征的数量如何影响每种方法的性能？所有方法均通过 10-20 个特征达到最佳性能。在离线仿真中，二维线性方法和一维非线性方法比一维线性方法显着提高了性能。一维线性方法没有这样做。这些离线结果表明一维非线性或二维线性光标功能将改善 BCI 系统的在线操作。
The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations).This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear).We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.