Title
Predicting the semantic category of internally generated words from neuromagnetic recordings.
Abstract
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.
Year
DOI
Venue
2015
10.1162/jocn_a_00690
Journal of cognitive neuroscience
Field
DocType
Volume
Brain mapping,Verbal fluency test,Communication,Cognitive psychology,Brain activity and meditation,Natural language processing,Language production,Artificial intelligence,Classifier (linguistics),Psychology,Semantics,Magnetoencephalography,Middle temporal gyrus
Journal
27
Issue
ISSN
Citations 
1
1530-8898
4
PageRank 
References 
Authors
0.42
14
4
Name
Order
Citations
PageRank
Irina Simanova140.42
Marcel A. J. van Gerven262.26
Robert Oostenveld3130080.66
Peter Hagoort430466.52