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Predicting the Semantic Category of Internally Generated Words from Neuromagnetic Recordings.
MedLine Citation:
PMID:  25061927     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
In this study, we explore the possibility to predict the semantic category of words from brain signals in a free word generation task. Participants produced single words from different semantic categories in a modified semantic fluency task. A Bayesian logistic regression classifier was trained to predict the semantic category of words from single-trial MEG data. Significant classification accuracies were achieved using sensor-level MEG time series at the time interval of conceptual preparation. Semantic category prediction was also possible using source-reconstructed time series, based on minimum norm estimates of cortical activity. Brain regions that contributed most to classification on the source level were identified. These were the left inferior frontal gyrus, left middle frontal gyrus, and left posterior middle temporal gyrus. Additionally, the temporal dynamics of brain activity underlying the semantic preparation during word generation was explored. These results provide important insights about central aspects of language production.
Authors:
Irina Simanova; Marcel A J van Gerven; Robert Oostenveld; Peter Hagoort
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-25
Journal Detail:
Title:  Journal of cognitive neuroscience     Volume:  -     ISSN:  1530-8898     ISO Abbreviation:  J Cogn Neurosci     Publication Date:  2014 Jul 
Date Detail:
Created Date:  2014-7-25     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8910747     Medline TA:  J Cogn Neurosci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1-11     Citation Subset:  -    
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