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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berkes, P., Wiskott, L.: Slow feature analysis yields a rich repertoire of complex cell properties. Journal of Vision 147, 579–602 (2005)
Blankertz, B., Lemm, S., Treder, M.S., Haufe, S., Müller, K.-R.: Single-trial analysis and classification of ERP components – a tutorial. NeuroImage (2010) ( in press)
Blaschke, T., Berkes, P., Wiskott, L.: What is the relationship between slow feature analysis and independent component analysis? Neural Computation 18(10), 2495–2508 (2006)
Höhne, J., Schreuder, M., Blankertz, B., Tangermann, M.: Two-dimensional auditory P300 Speller with predictive text system. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 4185–4188 (2010)
Kachenoura, A., Albera, L., Senhadji, L., Comon, P.: Ica: a potential tool for bci systems. IEEE Signal Processing Magazine, 25(1), 57–68 (2008)
Makeig, S., Jung, T.P., Bell, A.J., Ghahremani, D., Sejnowski, T.J.: Blind separation of auditory event-related brain responses into independent components. Proc. Natl. Acad. Sci. U.S.A. 94, 10979–10984 (1997)
Schreuder, M., Blankertz, B., Tangermann, M.: A New Auditory Multi-class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue. PLoS ONE 5(4) (2010)
Wiskott, L., Sejnowski, T.J.: Slow feature analysis: unsupervised learning of invariances. Neural Computation 14(4), 715–770 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dähne, S., Höhne, J., Schreuder, M., Tangermann, M. (2011). Slow Feature Analysis - A Tool for Extraction of Discriminating Event-Related Potentials in Brain-Computer Interfaces. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-21735-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21734-0
Online ISBN: 978-3-642-21735-7
eBook Packages: Computer ScienceComputer Science (R0)