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Main Title: Brain Oscillations and Functional Connectivity during Overt Language Production
Author(s): Ewald, Arne
Aristei, Sabrina
Nolte, Guido
Rahman, Rasha Abdel
Type: Article
Language Code: en
Abstract: In the present study we investigate the communication of different large scale brain sites during an overt language production task with state of the art methods for the estimation of EEG functional connectivity. Participants performed a semantic blocking task in which objects were named in semantically homogeneous blocks of trials consisting of members of a semantic category (e.g., all objects are tools) or in heterogeneous blocks, consisting of unrelated objects. The classic pattern of slower naming times in the homogeneous relative to heterogeneous blocks is assumed to reflect the duration of lexical selection. For the collected data in the homogeneous and heterogeneous conditions the imaginary part of coherency (ImC) was evaluated at different frequencies. The ImC is a measure for detecting the coupling of different brain sites acting on sensor level. Most importantly, the ImC is robust to the artifact of volume conduction. We analyzed the ImC at all pairs of 56 EEG channels across all frequencies. Contrasting the two experimental conditions we found pronounced differences in the theta band at 7 Hz and estimated the most dominant underlying brain sources via a minimum norm inverse solution based on the ImC. As a result of the source localization, we observed connectivity between occipito-temporal and frontal areas, which are well-known to play a major role in lexical-semantic language processes. Our findings demonstrate the feasibility of investigating interactive brain activity during overt language production.
Issue Date: 7-Jun-2012
Date Available: 6-Nov-2019
DDC Class: 150 Psychologie
Subject(s): overt language production
semantic interference
brain oscillations
functional connectivity
Journal Title: Frontiers in Psychology
Publisher: Frontiers Media S.A.
Publisher Place: Lausanne
Volume: 3
Article Number: 166
Publisher DOI: 10.3389/fpsyg.2012.00166
EISSN: 1664-1078
Appears in Collections:FG Maschinelles Lernen » Publications

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