Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis
Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis
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DOI：
10.1016/j.patrec.2007.01.002
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发表时间：
2007-06-01
影响因子：
5.1
通讯作者：
Pfurtscheller, Gert
中科院分区：
文献类型：
--
作者：
Brunner, Clemens;Naeem, Muhammad;Pfurtscheller, Gert
三个独立成分分析（伊卡）算法（Infomax，FastICA和SOBI）已与其他预处理方法进行了比较，以找出是否和在何种程度上空间滤波的EEG数据可以提高单次试验分类精度。作为参考方法，使用了共同空间模式（CSP）（一种监督方法，而所有伊卡算法都是无监督的）、双极推导和原始单极数据。除了只进行伊卡，减少组件的数量与PCA计算Infomax和FastICA的空间滤波器之前，多通道数据（22个通道）的8名受试者，包括两个会议记录在不同的日子，进行了分析。任务是在预定义的时间片（线索范式）内分别执行左手、右手、脚或舌头的运动想象。对于适应度的测量，计算了使用来自仅一个会话的数据的交叉验证结果以及模拟在线结果（代表会话到会话转移）的分类准确度。在后一种情况下，空间滤波器和分类器是为一个会话计算的，并应用于完全看不见的第二个会话。对于本研究中分析的数据，Infomax在交叉验证和模拟在线情况下都远远优于其他两个伊卡变体。另一方面，CSP在交叉验证结果中的分类准确率明显低于Infomax，而在模拟在线数据中没有统计学显著差异。在伊卡之前执行PCA改善了FastICA的结果，而Infomax的分类准确性显着下降。(c)2007 Elsevier B.V.保留所有权利。
Three independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) have been compared with other preprocessing methods in order to find out whether and to which extent spatial filtering of EEG data can improve single trial classification accuracy. As reference methods, common spatial patterns (CSP) (a supervised method, whereas all ICA algorithms are unsupervised), bipolar derivations and the original raw monopolar data were used. In addition to only performing ICA, the number of components was reduced with PCA before calculating a spatial filter for Infomax and FastICA.The multichannel data (22 channels) of eight subjects, consisting of two sessions recorded on different days, was analyzed. The task was to perform motor imagery of the left hand, right hand, foot or tongue, respectively, during predefined time slices (cued paradigm). For a measure of fitness, classification accuracies for both cross-validated results using data from just one session as well as simulated online results (representing the session-to-session transfer) were calculated. In the latter case, the spatial filters and classifiers were computed for one session and applied to the completely unseen second session.For the data analyzed in this study, Infomax outperformed the other two ICA variants by far, both in the cross-validated as well as in the simulated online case. CSP, on the other hand, yielded significantly lower classification accuracies than Infomax for the cross-validated results, whereas there is no statistically significant difference when it comes to simulated online data. Performing PCA before ICA improved the results in the case of FastICA, whereas the classification accuracies dropped significantly for Infomax. (c) 2007 Elsevier B.V. All rights reserved.