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EEG decoding of semantic category reveals distributed representations for single concepts

Murphy, Brian and Poesio, Massimo and Bovolo, Francesca and Bruzzone, Lorenzo and Dalponte, Michele and Lakany, Heba (2011) 'EEG decoding of semantic category reveals distributed representations for single concepts.' Brain and Language, 117 (1). pp. 12-22. ISSN 0093-934X

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Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100. ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon. © 2010 Elsevier Inc.

Item Type: Article
Uncontrolled Keywords: Concepts; Semantics; Categorisation; EEG; Data mining; Machine learning; Distributed representations; Exclusion of confounds
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 05 Mar 2013 16:15
Last Modified: 18 Aug 2022 11:13

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