EEG decoding of semantic category reveals distributed representations for single concepts  

Oleh:

Murphy, Brian ; Poesio, Massimo ; Bovolo, Francesca ; Bruzzone, Lorenzo ; Dalponte, Michele ; Lakany, Heba

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Brain and Language (Full Text) vol. 117 no. 1 (2011), page 12-22.
Topik: Concepts; Semantics; Categorisation; EEG; Data mining; Machine learning; Distributed representations; Exclusion of confounds
Fulltext: 117_01_Murphy.pdf (818.0KB)

Isi artikel

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 multipass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon.

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