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• W2041569133 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." @default.
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• W2041569133 date "2007-01-01" @default.
• W2041569133 modified "2023-09-24" @default.
• W2041569133 title "Inferring Functional Brain States Using Temporal Evolution of Regularized Classifiers" @default.
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• W2041569133 doi "https://doi.org/10.1155/2007/52609" @default.
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