Clustering of Spectral Patterns Based on EMD Components of EEG Channels with Applications to Neurophysiological Signals Separation
The notion of information separation in electrophysiological recordings is discussed. Whereas this problem is not entirely new, a novel approach to separate muscular interference from brain electrical activity observed in form of EEG is presented. The EEG carries brain activity in form of neurophysiological components which are usually embedded in much higher in power electrical muscle activity components (EMG, EOG, etc.). A novel multichannel EEG analysis approach is proposed in order to discover representative components related to muscular activity which are not related to ongoing brain activity but carry common patterns resulting from non-brain related sources. The proposed adaptive decomposition approach is also able to separate signals occupying same frequency bands what is usually not possible with contemporary methods.
KeywordsEmpirical Mode Decomposition Instantaneous Frequency Intrinsic Mode Function Zero Crossing Intrinsic Mode Function
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- 2.Rutkowski, T.M., Cichocki, A., Ralescu, A.L., Mandic, D.P.: Emotional states estimation from multichannel EEG maps. In: Wang, R., Gu, F., Shen, E. (eds.) Advances in Cognitive Neurodynamics: Proceedings of the International Conference on Cognitive Neurodynamics 2007. Neuroscience, pp. 695–698. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 3.Looney, D., Li, L., Rutkowski, T.M., Mandic, D.P., Cichocki, A.: Ocular artifacts removal from EEG using EMD. In: Wang, R., Gu, F., Shen, E. (eds.) Advances in Cognitive Neurodynamics: Proceedings of the International Conference on Cognitive Neurodynamics 2007, pp. 831–835. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 4.Niedermeyer, E., Da Silva, F.L. (eds.): Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 5th edn. Lippincott Williams & Wilkins (2004)Google Scholar
- 5.Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N.C., Tung, C., Liu, H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 454(1971), 903–995 (1998)MathSciNetCrossRefMATHGoogle Scholar
- 6.Rutkowski, T.M., Cichocki, A., Mandic, D.P.: Information Fusion for Perceptual Feedback: A Brain Activity Sonification Approach. In: Signal Processing Techniques for Knowledge Extraction and Information Fusion. Signals and Communication, pp. 261–274. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 7.Murtagh, F.: Multidimensional clustering algorithms. COMPSTAT Lectures 4 (1985)Google Scholar
- 8.R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008), http://www.R-project.org