Blind decomposition of multimodal evoked responses and DC fields

  • Authors:

  • Ricardo Vigário;Andreas Ziehe;Klaus-Robert Müller;Gerd Wübbeler;Lutz Trahms;Bruno-Marcel Mackert;Gabriel Curio;Veikko Jousmäki;Jaakko Särelä;Erkki Oja

  • Affiliations:

  • Fraunhofer Institut FIRST, Kekuléstr. 7, D-12489 Berlin, Germany;Fraunhofer Institut FIRST, Kekuléstr. 7, D-12489 Berlin, Germany;Fraunhofer Institut FIRST, Kekuléstr. 7, D-12489 Berlin, Germany;Physikalisch-Technische Bundesanstalt, Abbestr. 2-12, D-10587 Berlin, Germany;Physikalisch-Technische Bundesanstalt, Abbestr. 2-12, D-10587 Berlin, Germany;Neurophysics Group, Dept. of Neurology, Klinikum Benjamin Franklin, FU Berlin, Hindenburgdamm 30, 12203 Berlin, Germany;Neurophysics Group, Dept. of Neurology, Klinikum Benjamin Franklin, FU Berlin, Hindenburgdamm 30, 12203 Berlin, Germany;Brain Research Unit, Helsinki University of Technology, P.O. Box 2200, FIN-02015 HUT, Finland;Neural Networks Research Centre, Helsinki University of Technology, P.O. Box 5400, FIN-02015 HUT, Finland;Neural Networks Research Centre, Helsinki University of Technology, P.O. Box 5400, FIN-02015 HUT, Finland

  • Venue:
  • Exploratory analysis and data modeling in functional neuroimaging
  • Year:
  • 2003

Quantified Score

Hi-index 0.01

Visualization

Abstract

The advent of new brain mapping techniques, together with better and faster data storage capabilities, is generating a considerable amount of high-dimensional data. Suitable projecting or feature extraction mechanisms are required, able co reveal simple structures that may be easier to analyse than the complex brain activity that is often available to the physician, or brain researcher.In data analysis we often face the following dilemma: if we impose a too strong model on the data, we might only get the structure that we are imposing; if our model is too weak we might get no useful result at all. As there is no systematic answer to this fundamental problem for all situations, we will discuss about possibilities and limits of the new blind source separation (BSS) technique in the context of specific biomedical applications. Here a fair amount of physiological and physics knowledge is available and we can use this prior information to bias our solution - of course carefully avoiding to predetermine the solution.BSS methods, such as the ones based on independent component analysis (ICA) and temporal decorrelation (TD) methods have been shown to be an eificient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings, as well as the analysis of some evoked and spontaneous brain activity.This chapter reviews our recent results to the application of blind and not so blind source separation techniques to the analysis of evoked brain signals, elicited by sensory stimuli, and to the analysis of single trials of near DC brain fields.