ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization

Neuroimage. 2003 Dec;20(4):2010-30. doi: 10.1016/j.neuroimage.2003.07.024.

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.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Attention / physiology
  • Beta Rhythm*
  • Cortical Synchronization*
  • Data Interpretation, Statistical
  • Electroencephalography
  • Female
  • Humans
  • Magnetoencephalography / methods*
  • Magnetoencephalography / statistics & numerical data
  • Male
  • Movement / physiology*
  • Principal Component Analysis
  • Reproducibility of Results