Decoding word and category-specific spatiotemporal representations from MEG and EEG.

ABSTRACT: The organization and localization of lexico-semantic information in the brain has been debated for many years. Specifically, lesion and imaging studies have attempted to map the brain areas representing living versus nonliving objects, however, results remain variable. This may be due, in part, to the fact that the univariate statistical mapping analyses used to detect these brain areas are typically insensitive to subtle, but widespread, effects. Decoding techniques, on the other hand, allow for a powerful multivariate analysis of multichannel neural data. In this study, we utilize machine-learning algorithms to first demonstrate that semantic category, as well as individual words, can be decoded from EEG and MEG recordings of subjects performing a language task. Mean accuracies of 76% (c

SUBMITTER: Chan AM 

PROVIDER: S-EPMC3020243 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature