Spatio-spectral filters for improving the classification of single trial EEG
Spatio-spectral filters for improving the classification of single trial EEG
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DOI：
10.1109/tbme.2005.851521
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发表时间：
2005-09-01
影响因子：
4.6
通讯作者：
Müller, KR
中科院分区：
文献类型：
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作者：
Lemm, S;Blankertz, B;Müller, KR
在基于脑电(EEG)的脑机接口实验中，记录的数据通常是非常噪声、非平稳的，并且被伪影污染，这可能会恶化识别/分类方法。在本文中，我们对通用空间模式(CSP)算法进行了扩展，目的是减轻这些不利影响。特别地，我们提出了一种将CSP扩展到状态空间的方法，它利用了时延嵌入的方法。正如我们将展示的，这允许在每个电极位置单独调谐频率过滤器，从而产生改进的和更健壮的机器学习过程。在想象肢体运动实验的一组脑电记录上，验证了所提出的方法相对于原始CSP方法的优势。
Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.