Predicting the semantic category of internally generated words from neuromagnetic recordings
Number of pages
SourceJournal of Cognitive Neuroscience, 27, 1, (2015), pp. 35-45
Article / Letter to editor
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SW OZ DCC CO
SW OZ DCC AI
SW OZ DCC PL
PI Group Neurobiology of Language
Journal of Cognitive Neuroscience
Subject110 000 Neurocognition of Language; 110 007 PLUS: A neurocomputational model for the Processing of Linguistic Utterances based on the Unification-Space architecture; 110 009 The human brain and Chinese prosody; 110 012 Social cognition of verbal communication; 110 013 Binding and the MUC-model; 110 014 Public activities; 150 000 MR Techniques in Brain Function; Action, intention, and motor control; Cognitive artificial intelligence; DI-BCB_DCC_Theme 1: Language and Communication; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
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.
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