Abstract
A new formulation of principal component analysis (PCA) that considers group structure in the data is proposed as a variable down-selection method. Optimization of electrode channels is a key problem in brain-computer interfaces (BCI). BCI experiments generate large feature spaces compared to the sample size due to time limitations in EEG sessions. It is essential to understand the importance of the features in terms of physical electrode channels in order to design a high performance yet realistic BCI. The proposed algorithm produces a ranked list of original variables (electrode channels or features), according to their ability to discriminate movement imagery tasks. A linear discrimination analysis (LDA) classifier is applied to the selected variable subset. Evaluation of the down-selection method using synthetic datasets selected more than 83% of relevant variables. Classification of imagery tasks using real BCI datasets resulted in less than 19% classification error. Across-Group Variance (AGV) showed the best classification performance with the largest dimensionality reduction in comparison with other algorithms in common use.
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
Wolpaw, J.R., McFarland, D.J., Vaughan, T.M.: Brain–Computer Interface Research at the Wadsworth Center. IEEE Transactions On Rehabilitation Engineering 8, 222–226 (2000)
Pfurtscheller, G., Aranibar, A.: Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movements. Electroencephalogr. Clin. Neurophysiol. 46, 138–146 (1979)
Babiloni, C., et al.: Human movement-related potentials vs desynchronization of EEG alpha rhythm: A high-resolution EEG study. NeuroImage 10, 658–665 (1999)
Donchin, E., Spencer, K.M., Wijesinghe, R.: The Mental Prosthesis: Assessing the Speed of a P300-Based Brain–Computer Interface. IEEE Transactions on Rehabilitation Engineering 8, 174–179 (2000)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, Hoboken (2000)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant Features and the Subset Selection Problem. In: Cohen, W.W., Hirsh, H. (eds.) Proc. 11th Int. Conf. Machine Learning, pp. 121–129. Morgan Kaufmann, San Francisco (1994)
Yu, L., Liu, H.: Efficient feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning 5, 1205–1224 (2004)
Dillon, W.R., Mulani, N., Frederick, D.G.: On the use of component scores in the presence of group structure. Journal of Consumer Research 16, 106–112 (1989)
Kamrunnahar, M., Dias, N.S., Schiff, S.J.: Model-based Responses and Features in Brain Computer Interfaces. In: Proc. 30th IEEE EMBC, pp. 4482–4485. IEEE Press, Vancouver (2008)
Millán, J., Franzé, M., Mouriño, J., Cincotti, F., Babiloni, F.: Relevant EEG features for the classification of spontaneous motor-related tasks. Biol. Cybern. 86, 89–95 (2002)
Schröder, L.T., Weston, T., Bogdan, J., Birbaumer, M., Schölkopf, N.B.: Support vector channel selection in BCI. IEEE Trans. Biomed. Eng. 51, 1003–1010 (2004)
Schröder, M., Bogdan, M., Rosenstiel, W., Hinterberger, T., Birbaumer, N.: Automated EEG feature selection for brain computer interfaces. In: Proc. 1st IEEE EMBS Neural Eng., pp. 626–629. IEEE Press, Capri Island (2003)
Blankertz, B., et al.: The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 153–159 (2006)
Lai, C., Reinders, M.J.T., Wessels, L.: Random subspace method for multivariate feature selection. Pattern Recognition Letters 27, 1067–1076 (2006)
Luck, S.J.: An Introduction to the Event-Related Potential Technique. The MIT Press, Cambridge (2005)
Wolpaw, J.R., McFarland, D.J.: Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Nat. Acad. Sci. U.S.A. 101, 17849–17854 (2004)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, D.J., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002)
Schiff, S.J., Sauer, T., Kumar, R., Weinstein, S.L.: Neuronal spatiotemporal pattern discrimination: the dynamical evolution of seizures. Neuroimage 28, 1043–1055 (2005)
Flury, B.: A first course in multivariate statistics. Springer, New York (1997)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)
Xie, Z., Hu, Q., Yu, D.: Improved feature selection algorithm based on SVM and correlation. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1373–1380. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dias, N.S., Kamrunnahar, M., Mendes, P.M., Schiff, S.J., Correia, J.H. (2010). Variable Down-Selection for Brain-Computer Interfaces. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2009. Communications in Computer and Information Science, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11721-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-11721-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11720-6
Online ISBN: 978-3-642-11721-3
eBook Packages: Computer ScienceComputer Science (R0)