ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization |
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Authors: | Lee Po-Lei Wu Yu-Te Chen Li-Fen Chen Yong-Sheng Cheng Chou-Ming Yeh Tzu-Chen Ho Low-Tone Chang Mau-Song Hsieh Jen-Chuen |
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Institution: | Laboratory of Integrated Brain Research, Department of Medical Research and Education, Taipei Veterans General Hospital, Taipei, Taiwan. |
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Abstract: | The extraction of event-related oscillatory neuromagnetic activities from single-trial measurement is challenging due to the non-phase-locked nature and variability from trial to trial. The present study presents a method based on independent component analysis (ICA) and the use of a template-based correlation approach to extract Rolandic beta rhythm from magnetoencephalographic (MEG) measurements of right finger lifting. A single trial recording was decomposed into a set of coupled temporal independent components and corresponding spatial maps using ICA and the reactive beta frequency band for each trial identified using a two-spectrum comparison between the postmovement interval and a reference period. Task-related components survived dual criteria of high correlation with both the temporal and the spatial templates with an acceptance rate of about 80%. Phase and amplitude information for noise-free MEG beta activities were preserved not only for optimal calculation of beta rebound (event-related synchronization) but also for profound penetration into subtle dynamics across trials. Given the high signal-to-noise ratio (SNR) of this method, various methods of source estimation were used on reconstructed single-trial data and the source loci coherently anchored in the vicinity of the primary motor area. This method promises the possibility of a window into the intricate brain dynamics of motor control mechanisms and the cortical pathophysiology of movement disorder on a trial-by-trial basis. |
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Keywords: | Rolandic rhythm Motor cortex Single-trial Magnetoencephalography Event-related synchronization Independent component analysis (ICA) |
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