Decoding Sequence Learning from Single-Trial Intracranial EEG in Humans

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level...

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Detalles Bibliográficos
Autores principales: De Lucia, Marzia, Constantinescu, Irina, Sterpenich, Virginie, Pourtois, Gilles, Seeck, Margitta, Schwartz, Sophie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3235148/
https://www.ncbi.nlm.nih.gov/pubmed/22174850
http://dx.doi.org/10.1371/journal.pone.0028630

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3235148/
https://www.ncbi.nlm.nih.gov/pubmed/22174850
http://dx.doi.org/10.1371/journal.pone.0028630

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