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Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG...

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Detalles Bibliográficos
Autores principales: Zhang, Jianhua, Chen, Jian, Chen, Shengyong, Xiao, Gang, Li, Xiaoli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996362/
https://www.ncbi.nlm.nih.gov/pubmed/24803954
http://dx.doi.org/10.1155/2014/826019
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author Zhang, Jianhua
Chen, Jian
Chen, Shengyong
Xiao, Gang
Li, Xiaoli
author_facet Zhang, Jianhua
Chen, Jian
Chen, Shengyong
Xiao, Gang
Li, Xiaoli
author_sort Zhang, Jianhua
collection PubMed
description This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views' calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain.
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spelling pubmed-39963622014-05-06 Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions Zhang, Jianhua Chen, Jian Chen, Shengyong Xiao, Gang Li, Xiaoli Comput Math Methods Med Research Article This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views' calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain. Hindawi Publishing Corporation 2014 2014-04-03 /pmc/articles/PMC3996362/ /pubmed/24803954 http://dx.doi.org/10.1155/2014/826019 Text en Copyright © 2014 Jianhua Zhang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Jianhua
Chen, Jian
Chen, Shengyong
Xiao, Gang
Li, Xiaoli
Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions
title Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions
title_full Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions
title_fullStr Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions
title_full_unstemmed Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions
title_short Multimodal Spatial Calibration for Accurately Registering EEG Sensor Positions
title_sort multimodal spatial calibration for accurately registering eeg sensor positions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996362/
https://www.ncbi.nlm.nih.gov/pubmed/24803954
http://dx.doi.org/10.1155/2014/826019
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