Slow feature analysis: a tool for extraction of discriminating event-related potentials in brain-computer interfaces

Authors:
Sven Dähne;Johannes Höhne;Martijn Schreuder;Michael Tangermann
Affiliations:
Machine-Learning Department, Berlin Institute of Technology, Berlin, Germany;Machine-Learning Department, Berlin Institute of Technology, Berlin, Germany;Machine-Learning Department, Berlin Institute of Technology, Berlin, Germany;Machine-Learning Department, Berlin Institute of Technology, Berlin, Germany
Venue:
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Year:
2011

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Slow feature analysis: unsupervised learning of invariances

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What Is the Relation Between Slow Feature Analysis and Independent Component Analysis?

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Abstract

The unsupervised signal decomposition method Slow Feature Analysis (SFA) is applied as a preprocessing tool in the context of EEG based Brain-Computer Interfaces (BCI). Classification results based on a SFA decomposition are compared to classification results obtained on Principal Component Analysis (PCA) decomposition and to those obtained on raw EEG channels. Both PCA and SFA improve classification to a large extend compared to using no signal decomposition and require between one third and half of the maximal number of components to do so. The two methods extract different information from the raw data and therefore lead to different classification results. Choosing between PCA and SFA based on classification of calibration data leads to a larger improvement in classification performance compared to using one of the two methods alone. Results are based on a large data set (n=31 subjects) of two studies using auditory Event Related Potentials for spelling applications.