Optimizing spatial filters for robust EEG single-trial analysis
Optimizing spatial filters for robust EEG single-trial analysis
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DOI：
10.1109/msp.2008.4408441
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发表时间：
2008-01-01
影响因子：
14.9
通讯作者：
Mueller, Klaus-Robert
中科院分区：
文献类型：
--
作者：
Blankertz, Benjamin;Tomioka, Ryota;Mueller, Klaus-Robert
由于体积传导的原因，多通道脑电(EEG)记录给出的大脑活动图像相当模糊。因此，为了提高信噪比，空间滤波器在单次试验分析中是非常有用的。有来自机器学习和信号处理的强大方法允许以数据相关的方式为每个对象优化时空滤波器，而不是基于传感器几何结构的固定滤波器，例如拉普拉斯滤波器。在这里，我们阐述了共同空间模式(CSP)算法的理论背景，该算法是脑机接口(BCI)研究中的一种流行方法。除了回顾基本算法的几个变体外，我们还揭示了实现强大CSP性能的行业诀窍，简要阐述了CSP的理论方面，并展示了CSP类型的预处理在我们对柏林BCI(BBCI)项目的研究中的应用。
Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a rather blurred image of brain activity. Therefore spatial filters are extremely useful in single-trial analysis in order to improve the signal-to-noise ratio. There are powerful methods from machine learning and signal processing that permit the optimization of spatio-temporal filters for each subject in a data dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCI) research. Apart from reviewing several variants of the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance, briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing in our studies of the Berlin BCI (BBCI) project.