PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction
Guido Nolte, Andreas Ziehe, Frank Meinecke and Klaus-Robert Müller
In: Advances in Neural Information Processing Systems (2006) MIT Press , Cambridge, MA , pp. 1027-1034.

Abstract

When trying to understand the brain, it is of fundamental importance to analyse (e.g. from EEG/MEG measurements) what parts of the cortex interact with each other in order to infer more accurate models of brain activity. Common techniques like Blind Source Separation (BSS) can es- timate brain sources and single out artifacts by using the underlying as- sumption of source signal independence. However, physiologically inter- esting brain sources typically interact, so BSS will--by construction-- fail to characterize them properly. Noting that there are truly interacting sources and signals that only seemingly interact due to effects of volume conduction, this work aims to contribute by distinguishing these effects. For this a new BSS technique is proposed that uses anti-symmetrized cross-correlation matrices and subsequent diagonalization. The resulting decomposition consists of the truly interacting brain sources and sup- presses any spurious interaction stemming from volume conduction. Our new concept of interacting source analysis (ISA) is successfully demon- strated on MEG data.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Theory & Algorithms
ID Code:2443
Deposited By:Andreas Ziehe
Deposited On:22 November 2006