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Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG.
Dong, Li; Zhao, Lingling; Zhang, Yufan; Yu, Xue; Li, Fali; Li, Jianfu; Lai, Yongxiu; Liu, Tiejun; Yao, Dezhong.
Afiliação
  • Dong L; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Zhao L; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China.
  • Zhang Y; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, 611731, China.
  • Yu X; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Li F; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Li J; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Lai Y; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Liu T; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Yao D; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China.
Brain Topogr ; 34(4): 403-414, 2021 07.
Article em En | MEDLINE | ID: mdl-33950323
"Bad channels" are common phenomena during scalp electroencephalography (EEG) recording that arise due to various technique-related reasons, and reconstructing signals from bad channels is an inevitable choice in EEG processing. However, current interpolation methods are all based on purely mathematical interpolation theory, ignoring the neurophysiological basis of the EEG signals, and their performance needs to be further improved, especially when there are many scattered or adjacent bad channels. Therefore, a new interpolation method, named the reference electrode standardization interpolation technique (RESIT), was developed for interpolating scalp EEG channels. Resting-state and event-related EEG datasets were used to investigate the performance of the RESIT. The main results showed that (1) assuming 10% bad channels, RESIT can reconstruct the bad channels well; (2) as the percentage of bad channels increased (from 2% to 85%), the absolute and relative errors between the true and RESIT-reconstructed signals generally increased, and the correlations between the true and RESIT signals decreased; (3) for a range of bad channel percentages (2% ~ 85%), the RESIT had lower absolute error (approximately 2.39% ~ 33.5% reduction), lower relative errors (approximately 1.3% ~ 35.7% reduction) and higher correlations (approximately 2% ~ 690% increase) than traditional interpolation methods, including neighbor interpolation (NI) and spherical spline interpolation (SSI). In addition, the RESIT was integrated into the EEG preprocessing pipeline on the WeBrain cloud platform ( https://webrain.uestc.edu.cn/ ). These results suggest that the RESIT is a promising interpolation method for both separate and simultaneous EEG preprocessing that benefits further EEG analysis, including event-related potential (ERP) analysis, EEG network analysis, and strict group-level statistics.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Couro Cabeludo / Encéfalo Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Couro Cabeludo / Encéfalo Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article