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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 52609, 8 pages
http://dx.doi.org/10.1155/2007/52609
Research Article

Inferring Functional Brain States Using Temporal Evolution of Regularized Classifiers

1Functional Brain Imaging Unit, Tel Aviv Sourasky Medical Center, 6 Weizmann Street, Tel Aviv 64239, Israel
2Psychology Department and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
3Laboratory of Brain and Cognition, National Institute of Mental Health (NIMH), National Institute of Health (NIH), Bethesda 20892-1366, MD, USA
4The School of Computer Science, Tel Aviv University, P.O. Box 39040, Tel Aviv 69978, Israel

Received 18 February 2007; Accepted 16 July 2007

Academic Editor: Saied Sanei

Copyright © 2007 Andrey Zhdanov et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a framework for inferring functional brain state from electrophysiological (MEG or EEG) brain signals. Our approach is adapted to the needs of functional brain imaging rather than EEG-based brain-computer interface (BCI). This choice leads to a different set of requirements, in particular to the demand for more robust inference methods and more sophisticated model validation techniques. We approach the problem from a machine learning perspective, by constructing a classifier from a set of labeled signal examples. We propose a framework that focuses on temporal evolution of regularized classifiers, with cross-validation for optimal regularization parameter at each time frame. We demonstrate the inference obtained by this method on MEG data recorded from 10 subjects in a simple visual classification experiment, and provide comparison to the classical nonregularized approach.