Optimizing Spectral Filters for Single Trial EEG Classification
Optimizing Spectral Filters for Single Trial EEG Classification
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DOI：
10.1007/11861898_42
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发表时间：
2006-09
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通讯作者：
Ryota Tomioka;G. Dornhege;G. Nolte;K. Aihara;K. Müller
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作者：
Ryota Tomioka;G. Dornhege;G. Nolte;K. Aihara;K. Müller
针对单次试验脑电信号分类问题，提出了一种新的谱滤波优化算法。该算法旨在提高基于公共空间模式(CSP)的分类器的分类精度。该算法基于一个简单的统计标准，并允许用户合并任何关于信号频谱的先验信息。我们证明了在不同的预处理下，先验知识如何能够极大地提高分类性能，或者只是具有误导性。我们还给出了CSP算法的推广，以便在优化谱滤波后可以重新计算CSP空间投影。这导致了光谱和空间滤波器更新的迭代过程，不仅通过施加光谱滤波器，而且通过选择更好的空间投影来进一步提高分类精度。
We propose a novel spectral filter optimization algorithm for the single trial ElectroEncephaloGraphy (EEG) classification problem. The algorithm is designed to improve the classification accuracy of Common Spatial Pattern (CSP) based classifiers. The algorithm is based on a simple statistical criterion, and allows the user to incorporate any prior information one has about the spectrum of the signal. We show that with a different preprocessing, how a prior knowledge can drastically improve the classification or only be misleading. We also show a generalization of the CSP algorithm so that the CSP spatial projection can be recalculated after the optimization of the spectral filter. This leads to an iterative procedure of spectral and spatial filter update that further improves the classification accuracy, not only by imposing a spectral filter but also by choosing a better spatial projection.