Inferring functional brain states using temporal evolution of regularized classifiers.

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.

journal_name

Comput Intell Neurosci

authors

Zhdanov A,Hendler T,Ungerleider L,Intrator N

doi

10.1155/2007/52609

subject

Has Abstract

pub_date

2007-01-01 00:00:00

pages

52609

eissn

1687-5265

issn

1687-5273

pub_type

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