期刊
NEUROIMAGE
卷 54, 期 4, 页码 3028-3039出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2010.10.073
关键词
Language; Semantic category; Machine learning; Decoding; MEG; EEG
资金
- NDSEG Fellowship
- Frank H. Buck Scholarship
- Rappaport Fellowship
- NIH [NS18741]
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% (chance = 50%) and 83% (chance = 20%) were obtained for the decoding of living vs. nonliving category or individual words respectively. Furthermore, we utilize this decoding analysis to demonstrate that the representations of words and semantic category are highly distributed both spatially and temporally. In particular, bilateral anterior temporal, bilateral inferior frontal, and left inferior temporal-occipital sensors are most important for discrimination. Successful intersubject and intermodality decoding shows that semantic representations between stimulus modalities and individuals are reasonably consistent. These results suggest that both word and category-specific information are present in extracranially recorded neural activity and that these representations may be more distributed, both spatially and temporally, than previous studies suggest. (C) 2010 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据