A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG

A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG

Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the...

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Bibliographic Details
Main Authors: Wu, Wei (Contributor), Chen, Zhe (Contributor), Gao, Shangkai (Author), Brown, Emery N. (Contributor)
Other Authors: Harvard University- (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
Format: Article
Language:English
Published: Elsevier, 2016-04-04T23:10:37Z.
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