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2016 | OriginalPaper | Chapter

11. Comparative Study: Motor Area EEG and All-Channels EEG

Authors : Siuly Siuly, Yan Li, Yanchun Zhang

Published in: EEG Signal Analysis and Classification

Publisher: Springer International Publishing

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Abstract

This chapter reports a comparative study between motor area EEG and all-channels EEG for the three algorithms which are proposed in Chap. 10. In this chapter, we intend to investigate two particular issues: first, which of the three algorithms is the best for MI signal classification, and second, which EEG data, ‘motor area data or all-channels data’ is better for providing more information about MI signal classification.

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previous chapter Improving Prospective Performance in MI Recognition: LS-SVM with Tuning Hyper Parameters

next chapter Optimum Allocation Aided Naïve Bayes Based Learning Process for the Detection of MI Tasks

Abdulkadir, S. (2009) ‘Multiclass least-square support vector machines for analog modulation classification’, Expert System with Applications, Vol. 36, pp. 6681–6685.

BCI competition III, 2005, http://www.bbci.de/competition/iii.

Blankertz, B., Muller, K.R., Krusienki, D. J., Schalk, G., Wolpaw, J.R., Schlogl, A., Pfurtscheller, S., Millan, J. De. R., Shrooder, M. and Birbamer, N. (2006) ‘The BCI competition III: validating alternative approaches to actual BCI problems’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 14, no. 2, pp. 153–159.

Caesarendra, W., Widodo, A. and Yang, B.S. (2010) ‘Application of relevance vector machine and logistic regression for machine degradation assessment’, Mechanical Systems and Signal Processing, Vol. 24, pp. 1161–1171.

Chandaka, S., Chatterjee, A. and Munshi, S. (2009) ‘Cross-correlation aided support vector machine classifier for classification of EEG signals’, Expert System with Applications, Vol. 36, pp. 1329–1336.

Dutta, S., Chatterjee, A. and Munshi, S. (2010) ‘Correlation techniques and least square support vector machine combine for frequency domain based ECG beat classification’, Medical Engineering and Physics, Vol. 32, no. 10, pp. 1161–1169.

Hosmer, D.W. and Lemeshow, S. (1989) Applied logistic regression, Wiley, New York.

Oostenveld, R. & Praamstra, P. (2001) ‘The five percent electrode system for high-resolution EEG and ERD measurements’, Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology, Vol. 112, no. 4, pp. 713–719.

Sander T H, Leistner S, Wabnitz H, Mackert B M, Macdonald R and Trahms L (2010) ‘Cross-correlation of motor activity signals from dc-magnetoencephalography, near-infrared spectroscopy and electromyography’, Computational Intelligence and Neuroscience, doi:10.1155/2010/78527.

Siuly, Li, Y. and Wen, P. (2009) ‘Classification of EEG signals using Sampling Techniques and Least Square Support Vector Machines’, RSKT 2009, LNCS 5589, pp. 375–382.

Siuly, Li, Y. and Wen, P. (2010) ‘Analysis and classification of EEG signals using a hybrid clustering technique’, Proceedings of the 2010 IEEE/ICME International Conference on Complex Medical Engineering (CME2010), pp. 34–39.

Siuly, Li, Y. and Wen, P. (2011a) ‘Clustering technique-based least square support vector machine for EEG signal classification’, Computer Methods and Programs in Biomedicine, Vol. 104, Issue 3, pp. 358–372.

Siuly, Li, Y. and Wen, P. (2011b) ‘EEG signal classification based on simple random sampling technique with least square support vector machines’, International journal of Biomedical Engineering and Technology, Vol. 7, no. 4, pp. 390–409.

Siuly and Y. Li, (2012) ‘Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain computer interface’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 20, no. 4, pp. 526–538.

Siuly, Y. Li, and P. Wen, (2013) ‘Identification of Motor Imagery Tasks through CC-LR Algorithm in Brain Computer Interface’, International Journal of Bioinformatics Research and Applications, Vol.9, no. 2, pp. 156–172.

Siuly, Y. Li, and P. Wen, (2014) ‘Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface’, Computer Methods and programs in Biomedicine, Vol. 113, no. 3, pp. 767–780.

Siuly, Y. Li, and P. Wen, (2014) ‘Comparisons between Motor Area EEG and all-Channels EEG for Two Algorithms in Motor Imagery Task Classification’, Biomedical Engineering: Applications, Basis and Communications (BME), Vol. 26, no. 3, pp. 1450040 (10 pages).

Song L, Epps J, (2007) ‘Classifying EEG for brain-computer interface: learning optimal filters for dynamical features’, Comput Intell and Neurosci, Article ID 57180, 11 pages, doi:10.1155/2007/57180, 2007.

Subasi, A. and Ercelebi, E. (2005) ‘Classification of EEG signals using neural network and logistic regression’, Computer Methods and Programs in Biomedicine, Vol. 78, pp. 87–99.

Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D. and Vandewalle, J. (2002) Least Square Support Vector Machine, World Scientific, Singapore.

Thissen, U., Ustun, B., Melssen, W. J. and Buydens, L. M. C. (2004) ‘Multivariate calibration with least-square support vector machines’, Analytical Chemistry, Vol. 76, pp. 3099–3105.

Wang, S. & James, C. J., (2007) ‘Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis’, Computational Intelligence and Neuroscience, Article ID 41468, 9 pages, doi:10.1155/2007/41468.

Wu, W., Gao, X., Hong, B. and Gao, S. (2008) ‘Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL)’, IEEE Transactions on Biomedical Engineering, Vol. 55, no. 6, pp. 1733–1743.

Xie, L., Ying, Y. and Ying, T. (2009) ‘Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-square support vector machines and other chemometrics’, Journal of Food Engineering, Vol. 94, pp. 34–39.

Title: Comparative Study: Motor Area EEG and All-Channels EEG
Authors: Siuly Siuly
Yan Li
Yanchun Zhang
Publisher: Springer International Publishing
Book: EEG Signal Analysis and Classification
Print ISBN: 978-3-319-47652-0

Electronic ISBN: 978-3-319-47653-7

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-47653-7_11

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