Spatial filter selection for EEG-based communication
Spatial filter selection for EEG-based communication
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DOI：
10.1016/s0013-4694(97)00022-2
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发表时间：
1997-09-01
期刊：
影响因子：
--
通讯作者：
Wolpaw, JR
中科院分区：
文献类型：
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作者：
McFarland, DJ;McCane, LM;Wolpaw, JR
个人可以学会控制感觉运动皮层上记录的脑电图中μ节律活动的幅度，并使用它来将光标移动到视频屏幕上的目标。光标移动的速度和准确性取决于控制信号的一致性以及通过空间和时间滤波方法实现的信噪比，所述空间和时间滤波方法在活动转换成光标移动之前提取活动。本研究比较了替代的空间滤波方法。当训练有素的受试者将光标移动到视频屏幕的顶部或底部边缘处的目标时收集的64通道EEG数据通过四种不同的空间滤波器离线分析，即标准耳参考、共同平均参考（CAR）、小拉普拉斯算子（3cm到周围电极组）和大拉普拉斯算子（6cm到周围电极组）。CAR和大拉普拉斯方法被证明能够最好地区分顶部和底部目标。它们明显上级耳参照法。大拉普拉斯算子和小拉普拉斯算子方法之间的性能差异可能表明前者更好地匹配EEG控制信号的地形范围。作为一个整体的结果表明，适当的空间滤波器的选择，最大限度地提高信噪比，从而提高速度和准确性的EEG为基础的通信的重要性。(C)1997 Elsevier Science爱尔兰有限公司
Individuals can learn to control the amplitude of mu-rhythm activity in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The speed and accuracy of cursor movement depend on the consistency of the control signal and on the signal-to-noise ratio achieved by the spatial and temporal filtering methods that extract the activity prior to its translation into cursor movement. The present study compared alternative spatial filtering methods. Sixty-four channel EEG data collected while well-trained subjects were moving the cursor to targets at the top or bottom edge of a video screen were analyzed offline by four different spatial filters, namely a standard ear-reference, a common average reference (CAR), a small Laplacian (3 cm to set of surrounding electrodes) and a large Laplacian (6 cm to set of surrounding electrodes). The CAR and large Laplacian methods proved best able to distinguish between top and bottom targets. They were significantly superior to the ear-reference method. The difference in performance between the large Laplacian and small Laplacian methods presumably indicated that the former was better matched to the topographical extent of the EEG control signal. The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based communication. (C) 1997 Elsevier Science Ireland Ltd.