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Abstrakty
One source of EEG data quality deterioration is noise. The others are artifacts, such as the eye blinking, oculogyration, heart beat, or muscle activity. All these factors mentioned above contribute to the disappointing and poor quality of EEG signals. There are some solutions which allow increase of this signals quality. One of them is Common Spatial Patterns. Some scientific papers report that CSP can only be effectively used if there are many electrodes available. The aim of this paper is to use CSP method applied in the process of creating a brain computer interface in order to find out if there are any benefits of using this method in 3 channels BCI system.
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Tom
Strony
56--63
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
autor
- Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland
Bibliografia
- [1] Rejer, I., Gorski, P.: Independent Component Analysis for EEG data preprocessing – algorithms comparison, Computer Information Systems and Industrial Management, Lectures Notes in Computer Science, vol. 8104, pp.108-119, Springer, 2013.
- [2] Rejer, I., Gorski, P.: Benefits of ICA in the case of a few channel EEG, Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milano (in print), 2015.
- [3] Gorski, P.: Performance comparison of ICA algorithms for audio blind source separation, Przegląd Elektrotechniczny, vol.91 no.2, 2015.
- [4] Blankertz, B., Losch, F., Krauledat, M., Dornhege, G., Curio, G., Mller, K. R.: The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects, IEEE Trans Biomed, 2008.
- [5] Ramoser, H., Mller-Gerking, J., Pfurtscheller, G.: Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement Rehabilitation Engineering, vol.8 no. 4, 2000.
- [6] Haiping, L., Konstantinos, N., Plataniotis, K. N., Venetsanopoulos, N.: Regularized Common Spatial Patterns with Generic Learning for EEG Signal Classification, in Proc. EMBC, 2009.
- [7] Ang, K. K., Chin, Z. Y., Zhang, H., Guan, C.: Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface, Neural Networks, pp.2930-2397, 2008.
- [8] Lei, X., Yang, P., Xu, P., Liu, T. J., Yao, D. Z.: Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface, Journal of Electronic Science And Technology of China, vol.7 no.1. 2009.
- [9] Ge, S., Wang, R., Yu, D.: Classification of Four-Class Motor Imagery Employing Single-Channel, Electroencephalography. PLoS ONE, vol. 9 no.6, 2014.
- [10] Lotte, F., Guan, C.: Regularizing Common Spatial Patterns to Improve BCI Designs: Theory and Algorithms, Biomedical Engineering, IEEE, vol.58 no.2, pp. 355-362, 2010.
- [11] Graimann, B., Allison, B. Z., Pfurtschelle, G.: Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction, Revolutionizing Human-Computer Interaction, pp. 312-314, 2010.
- [12] Arvaneh, M., Guan, C., Ang, K. K., Hiok Chai Quek: Spatially sparsed Common Spatial Pattern to improve BCI performance, Acoustics, Speech and Signal Processing (ICASSP), pp. 2412-2415, 2011.
- [13] Rejer, I.: Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand, Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, Advances in Intelligent Systems and Computing, Springer, Vol. 226, pp. 579-589, 2013.
- [14] Rejer, I.: Genetic Algorithm with Aggressive Mutation for Feature Selection in BCI Feature Space, Pattern Anal Applic, DOI 10.1007/s10044-014-0425-3, 2015.
- [15] Data set III, II BCI Competition, motor imaginary, http://bbci.de/competition/ii/index.html
- [16] Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B (Methodological), vol.58, no.1, pp: 267288, 1996.
- [17] Li, F., Yang, Y., Xing, E. P.: From Lasso regression to Feature Vector Machine, Advances in Neural Information Processing Systems, vol.18, 2005.
Typ dokumentu
Bibliografia
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