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

Chia-Feng Lu, C.Y. Hung, P.J. Tseng, L.T. Lin, Z.Y. Wang, Y.T. Wu

Research output: Contribution to conferenceOther

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.
Original languageEnglish
Pages592-595
Number of pages4
Publication statusPublished - 2009
Externally publishedYes

Fingerprint

Biomedical engineering
Electroencephalography
Prosthetics
Patient rehabilitation
Physics
Decomposition
Bioelectric potentials
Brain

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

Cite this

@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",

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TY - CONF

T1 - Recognition of arm-movement electroencephalography (EEG) using motor-related intrinsic mode functions filtering and cross mutual information

T2 - World Congress on Medical Physics and Biomedical Engineering: Neuroengineering, Neural Systems, Rehabilitation and Prosthetics

AU - Lu, Chia-Feng

AU - Hung, C.Y.

AU - Tseng, P.J.

AU - Lin, L.T.

AU - Wang, Z.Y.

AU - Wu, Y.T.

N1 - 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; Müller, K.R., Krauledat, M., Dornhege, G., Machine learning techniques for brain computer interfaces (2004) Biomed Tech, 49, pp. 11-22

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Cross mutual infromation

KW - Electroencephalography

KW - Intrinsic mode functions

KW - Arm movements

KW - Band pass filtering

KW - Classification rates

KW - EEG signals

KW - Empirical mode decomposition

KW - Filtered signals

KW - Healthy subjects

KW - MU rhythm

KW - Mutual informations

KW - Peak frequencies

KW - Subject-specific

KW - Two channel

KW - Bandpass filters

KW - Biomedical engineering

KW - Electrophysiology

KW - Physics

KW - Prosthetics

KW - Signal analysis

KW - Signal processing

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M3 - Other

SP - 592

EP - 595

ER -