Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis

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.