Abstract
Subject dependent nature of electroencelography (EEG) signal elicits in the imagination task cause the drop of accuracy of the classifier in the brain-computer interface when the system apply in different subjects or crossing session experiment. The main components that have effect most in this problem are spatial-spectral-temporal parameter of the EEG signal that need to extract to find the optimal solution in the BCI system. In this paper we proposed a method for extracting the optimal parameters based on particle swarm optimization algorithm. First EEG signals were enhanced by Laplace and band pass filter. Optimal spatio-spectral-temporal component of Principle Component Analysis were search by Particle Swarm Optimization (PSO) using Short Time Fourier Transform features and classification error rate from Support Vector Machine (SVM) as fitness function. With optimal parameters, principle component from the STFT features were extracted and combined into single optimal feature vector. 5 fold-cross validations are applied to SVM.
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References
Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proceedings of the IEEE 89, 1123–1134 (2001)
Lotte, F., Congedo, M.: Evolution of brain-computer interfaces: going beyond classic motor physiology. Journal of Neural. 27, 1–21 (2007)
Pregenzer, M., Pfurtscheller, G.: Frequency component selection for an EEG-based brain to computer interface. IEEE Transactions on 7, 413–419 (1999)
Hasan, B.: Multi-Objective Particle Swarm Optimization for Channel Selection in Brain-Computer Interfaces. In: The UK Workshop on Computational Intelligence, pp. 2–7 (2009)
Satti, A., Coyle, D.: Spatio-spectral & temporal parameter searching using class correlation analysis and particle swarm optimization for a brain computer interface. Systems, Man and Cybernetics, 1731–1735 (2009)
Vuckovic, A., Sepulveda, F.: Quantification and visualization of differences between two motor tasks based on energy density maps for brain-computer interface applications. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 119, 446–458 (2008)
Durka, P., Zygiereicz, J., Klekowicz, H., et al.: On the statistical significance of event-related EEG desnchronization and synchronization in time-frequency plane. IEEE Transaction on Bio-Medical Engineering 51, 1167–1175 (2004)
Fazil, S., Popescu, F., Danóczy, M., et al.: at al.: Subject-independent mental state classification in single trials. Neural Networks: the Official Journal of the International Neural Network Society 22, 1305–1312 (2009)
Krusienki, D., Grosse-Wentrup, M., Galán, F., et al.: Critical issue in state-of-the-art brain-computer interface signal processing. Journal of Neural Engineering 8, 1–8 (2011)
Grandchamp, R., Delorme, A.: Single-trial normalization for event-related spectral decomposition reduce sensitivity to noisy trials. Frontier in Psychology 2, 1–14 (2011)
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Chum, P., Park, SM., Ko, KE., Sim, KB. (2012). Particle Swarm Optimization Based Optimal Spatial-Spectral-Temporal Component Search in Motor Imagery Brain-Computer Interface. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_59
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DOI: https://doi.org/10.1007/978-3-642-32645-5_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32644-8
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