摘要：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.
