Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA)

P. L. Lee, Y. T. Wu, L. F. Chen, S. S. Chen, T. C. Yeh, L. T. Ho, J. C. Hsieh

Research output: Contribution to conferencePaper

Abstract

The human brain ∼20-Hz rhythm measured by electroencephalography (EEG) and magnetoencephalography (MEG) has been used as a clinical examinaion index of motor function which originates in the anterior bank of the central sulcus in human brain. In human voluntary movement, it is composed of three phases, planning, execution and recovery which has been suggested that localized event-related alpha desynchronization (ERD) upon movement can be viewed as an EEG/MEG correlate of an activated cortical motor network, servicing planning and execution, while beta event-related synchronization (ERS) may reflect deactivation/inhibition during the recovery phase in the underlying cortical network, The single-trial detection of ∼20Hz rhythm is changlled because of its low signal amplitude and its signal-to-noise ration (SNR) in EEG/MEG measured neural activities. This present study proposes a method based on independent component analysis (ICA) for extraction of the sensorimotor rhythm from magnetoencephalographic (MEG) measurements of a right finger lifting task in a single trail. ICA decomposes a single trial recording into a set of temporal independent components (IC) and corresponding spatial maps in which the task-related components are selected by visual inspection. Pertinent ICs are then selected by visual inspection to reconstruct task-related beta oscillatory activity which is then subjected to beta rebound quantification and source estimation in further analyses. Since the event-related oscillatory activity of human brain is related to subject's performance and state, the ICA-based single trial method enables the possibility of studying in a single-trial, which in turn may shed light on the intricate dynamics of the brain.

Original languageEnglish
Pages1081-1085
Number of pages5
Publication statusPublished - 2003 Sep 24
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 2003 Jul 202003 Jul 24

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period03/7/2003/7/24

Fingerprint

Magnetoencephalography
Independent component analysis
Brain
Synchronization
Electroencephalography
Inspection
Planning
Recovery

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Lee, P. L., Wu, Y. T., Chen, L. F., Chen, S. S., Yeh, T. C., Ho, L. T., & Hsieh, J. C. (2003). Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA). 1081-1085. Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.

Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA). / Lee, P. L.; Wu, Y. T.; Chen, L. F.; Chen, S. S.; Yeh, T. C.; Ho, L. T.; Hsieh, J. C.

2003. 1081-1085 Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.

Research output: Contribution to conferencePaper

Lee, PL, Wu, YT, Chen, LF, Chen, SS, Yeh, TC, Ho, LT & Hsieh, JC 2003, 'Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA)' Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States, 03/7/20 - 03/7/24, pp. 1081-1085.
Lee PL, Wu YT, Chen LF, Chen SS, Yeh TC, Ho LT et al. Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA). 2003. Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.
Lee, P. L. ; Wu, Y. T. ; Chen, L. F. ; Chen, S. S. ; Yeh, T. C. ; Ho, L. T. ; Hsieh, J. C. / Single-trial analysis of post-movement MEG beta synchronization using independent component analysis (ICA). Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.5 p.
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