Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.
Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.
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DOI:
10.1016/j.jneumeth.2014.04.020
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发表时间:
2014-05-30
影响因子:
3
通讯作者:
Diamond, Solomon G.
中科院分区:
文献类型:
--
作者:
Giacometti, Paolo;Perdue, Katherine L.;Diamond, Solomon G.
Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. An algorithm is introduced for automatic calculation of the International 10–20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10–20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest.
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影响因子:
5.7
作者:
Fischl, B;Sereno, MI;Dale, AM
通讯作者:
Dale, AM
影响因子:
5.7
作者:
Reuter, Martin;Schmansky, Nicholas J.;Rosas, H. Diana;Fischl, Bruce
通讯作者:
Fischl, Bruce
影响因子:
3.5
作者:
Giacometti, Paolo;Diamond, Solomon G.
通讯作者:
Diamond, Solomon G.
影响因子:
5.7
作者:
Dale, AM;Fischl, B;Sereno, MI
通讯作者:
Sereno, MI
影响因子:
5.7
作者:
Fischl, Bruce
通讯作者:
Fischl, Bruce