Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization

Jianting Cao*, Noboru Murata, Shun Ichi Amari, Andrzej Cichocki, Tsunehiro Takeda

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


This paper presents a novel method for decomposing and localizing unaveraged single-trial magnetoencephalographic data based on the independent component analysis (ICA) approach associated with pre- and post-processing techniques. In the pre-processing stage, recorded single-trial raw data are first decomposed into uncorrelated signals with the reduction of high-power additive noise. In the stage of source separation, the decorrelated source signals are further decomposed into independent source components. In the post-processing stage, we perform a source localization procedure to seek a single-dipole map of decomposed individual source components, e.g., evoked responses. The first results of applying the proposed robust ICA approach to single-trial data with phantom and auditory evoked field tasks indicate the following. (1) A source signal is successfully extracted from unaveraged single-trial phantom data. The accuracy of dipole estimation for the decomposed source is even better than that of taking the average of total trials. (2) Not only the behavior and location of individual neuronal sources can be obtained but also the activity strength (amplitude) of evoked responses corresponding to a stimulation trial can be obtained and visualized. Moreover, the dynamics of individual neuronal sources, such as the trial-by-trial variations of the amplitude and location, can be observed.

Original languageEnglish
Pages (from-to)255-277
Number of pages23
Issue number1-4
Publication statusPublished - 2002 Dec


  • Auditory evoked fields (AEF)
  • Independent component analysis (ICA)
  • Magnetoencephalography (MEG)
  • Phantom experiment
  • Robust pre-whitening technique
  • Single-dipole source localization
  • Single-trial data analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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