@conference{6f3cc9c880cb48d18b28ae51ff982e80,
title = "Recognition of arm-movement electroencephalography (EEG) using motor-related intrinsic mode functions filtering and cross mutual information: World Congress on Medical Physics and Biomedical Engineering: Neuroengineering, Neural Systems, Rehabilitation and Prosthetics",
abstract = "In this paper, we propose trial-specific and subject-specific filters to extract the motor-related compartment for the recognition of EEG signals induced by the left- or right-arm movement. Such motor-related filters are the intrinsic mode functions (IMF), which were produced by the decomposition of signals in C3 or C4 motor channels using the empirical mode decomposition (EMD), with the peak frequency pertaining to the mu rhythm within alpha band (8-12Hz) or beta band (16-25Hz). After these trial-specific and subject-specific filters were applied on all channels, the cross mutual information (CMI) of filtered signals between any two channels was computed. The average classification rates for five healthy subjects obtained from the proposed filters related to the alpha and beta bandpass filtering with whole-brain CMI maps were 77.4% and 88.3%, respectively, which were superior to the 72.2%, and 77.7% obtained from the same filtering but with only CMI vectors related to motor signals in C3 and C4, respectively.",
keywords = "Cross mutual infromation, Electroencephalography, Intrinsic mode functions, Arm movements, Band pass filtering, Classification rates, EEG signals, Empirical mode decomposition, Filtered signals, Healthy subjects, MU rhythm, Mutual informations, Peak frequencies, Subject-specific, Two channel, Bandpass filters, Biomedical engineering, Electrophysiology, Physics, Prosthetics, Signal analysis, Signal processing",
author = "Chia-Feng Lu and C.Y. Hung and P.J. Tseng and L.T. Lin and Z.Y. Wang and Y.T. Wu",
note = "會議代碼: 81640 Export Date: 31 March 2016 通訊地址: Wu, Y. T.; Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, Taiwan; 電子郵件： ytwu@ym.edu.tw 參考文獻: Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Liu, H.H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis (1998) Proc R Soc Lond A, 454, pp. 903-995; Chen, C.C., Hsieh, J.C., Wu, Y.Z., Lee, P.L., Chen, S.S., Niddam, D., Yeh, T.C., Wu, Y.T., Mutual-information based approach for neural connectivity during self-paced finger lifting task (2008) Human Brain Mapping, 29 (3), pp. 265-280; Duda, R.O., Hart, P.E., Stork, D.G., (2001) Pattern Classification, pp. 117-120. , 2nd Ed, John Wiley &Sons, Inc; Toro, R., Fox, P.T., Paus, T., Functional coactivation map of the human brain (2008) Cerebral Cortex, 18, pp. 2553-2559; Oppenheim, A.V., Schafer, R.W., (1989) Discrete-time Signal Processing, pp. 542-546. , Prentice-Hall International, Inc; M{\"u}ller, K.R., Krauledat, M., Dornhege, G., Machine learning techniques for brain computer interfaces (2004) Biomed Tech, 49, pp. 11-22",
year = "2009",
language = "English",
pages = "592--595",
}