Inferring functional brain states using temporal evolution of regularized classifiers

Andrey Zhdanov*, Talma Hendler, Leslie Ungerleider, Nathan Intrator

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Article number52609
JournalComputational Intelligence and Neuroscience
StatePublished - 2007


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