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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Elsevier
2016
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| Online Access: | http://hdl.handle.net/1721.1/102159 https://orcid.org/0000-0003-2668-7819 |