Using a structured-light 3D scanner to improve EEG source modeling with more accurate electrode positions
Publication year
2019Source
Journal of Neuroscience Methods, 326, (2019), article 108378ISSN
Publication type
Article / Letter to editor
Related datasets
Display more detailsDisplay less details
Organization
PI Group MR Techniques in Brain Function
Journal title
Journal of Neuroscience Methods
Volume
vol. 326
Subject
150 000 MR Techniques in Brain FunctionAbstract
Background In this study, we evaluated the use of a structured-light 3D scanner for EEG electrode digitization. We tested its accuracy, robustness and evaluated its practical feasibility. Furthermore, we assessed how 3D scanning of EEG electrode positions affects the accuracy of EEG volume conduction models and source localization. New method To assess the improvement in electrode positions and source results, we compared the electrode positions both at the scalp level and by quantifying source model accuracy between the 3D scanner, generic template, and cap-specific electrode positions. Results and comparison with existing methods The use of the 3D scanner significantly improves the accuracy of EEG electrode positions to a median error of 9.4 mm and maximal error of 32.8 mm, relative to the custom (median error of 10.9 mm, maximal error 39.1 mm) and manufacturer’s template positions (median error of 13.8 mm, maximal error 57.0 mm). The relative difference measure (RDM) of the EEG source model averaged over the brain improves from 0.18 to 0.11. The dipole localization error averaged over the brain improves from 11.4 mm to 7.0 mm. Conclusion A structured-light 3D scanner improves the electrode position accuracy and thereby the EEG source model accuracy. It is more affordable than systems currently used for this, and allows for robust and fast digitization. Therefore, we consider it a cost and time-efficient way to improve EEG source reconstruction.
This item appears in the following Collection(s)
- Academic publications [246764]
- Donders Centre for Cognitive Neuroimaging [4043]
- Electronic publications [134215]
- Open Access publications [107738]
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.