EEG decoding of semantic category reveals distributed representations for single concepts

Here we present a collection of advanced data-mining techniques that allows the semantics 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. 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 100ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide a new alternative to fMRI for fine-grained investigations of the conceptual lexicon