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Group-wise Stationary Subspace Analysis - A Novel Method for Studying Non-Stationarities
Citation key Samek20110
Author Samek, W., Kawanabe, M., and Vidaurre, C.
Title of Book Proceedings of 5th International Brain-Computer Interface Conference 2011
Year 2011
Abstract In this paper we present an extension of the recently proposed Stationary Subspace Analy- sis (SSA). This novel method solves the problem how to group signals from different conditions and/or subjects to find stationary subspaces. The original SSA approach does not offer a natural way to group data and therefore better define the non-stationarities of interest. This drawback is solved with group-wise SSA (gwSSA) and demonstrated with a simple but illustrative example: the classification of BCI data. If not treated correctly the BCI tasks are considered as non-stationarities in SSA, which complicates its use for classification purposes. We show how, by correctly defining groups, non-stationarities of interest can be extracted. In this paper, the application is in multi-class signals, where the groups are properly defined to even improve classification performance.
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