Inferring functional brain states using temporal evolution of regularized classifiers

  • Authors:

  • Andrey Zhdanov;Talma Hendler;Leslie Ungerleider;Nathan Intrator

  • Affiliations:

  • Functional Brain Imaging Unit, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel and The School of Computer Science, Tel Aviv University, Tel Aviv, Israel;Functional Brain Imaging Unit, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel and Psychology Department and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel;Laboratory of Brain and Cognition, National Institute of Mental Health, National Institute of Health, Bethesda, MD;The School of Computer Science, Tel Aviv University, Tel Aviv, Israel

  • Venue:
  • Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
  • Year:
  • 2007

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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.