EEG electrodes and where to find them: automated localization from 3D scans.

Autor:	Tveter M; Department of Neurology, Oslo University Hospital, Oslo, Norway.; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway., Tveitstøl T; Department of Neurology, Oslo University Hospital, Oslo, Norway.; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway., Nygaard T; Department of Technology Systems, University of Oslo, Oslo, Norway., Pérez T AS; Department of Neurology, Oslo University Hospital, Oslo, Norway.; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway., Kulashekhar S; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.; BioMag Laboratory, Helsinki University Hospital Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland., Bruña R; Department of Radiology, Rehabilitation and Physiotherapy, School of Medicine, Universidad Complutense de Madrid, Madrid, Spain., Hammer HL; Department of Holistic Systems, SimulaMet, Oslo, Norway.; Department of Computer Science, Oslo Metropolitan University, Oslo, Norway., Hatlestad-Hall C; Department of Neurology, Oslo University Hospital, Oslo, Norway., Hebold Haraldsen IRJ; Department of Neurology, Oslo University Hospital, Oslo, Norway.
Jazyk:	angličtina
Zdroj:	Journal of neural engineering [J Neural Eng] 2024 Sep 30; Vol. 21 (5). Date of Electronic Publication: 2024 Sep 30.
DOI:	10.1088/1741-2552/ad7c7e
Abstrakt:	Objective. The accurate localization of electroencephalography (EEG) electrode positions is crucial for accurate source localization. Recent advancements have proposed alternatives to labor-intensive, manual methods for spatial localization of the electrodes, employing technologies such as 3D scanning and laser scanning. These novel approaches often integrate magnetic resonance imaging (MRI) as part of the pipeline in localizing the electrodes. The limited global availability of MRI data restricts its use as a standard modality in several clinical scenarios. This limitation restricts the use of these advanced methods. Approach. In this paper, we present a novel, versatile approach that utilizes 3D scans to localize EEG electrode positions with high accuracy. Importantly, while our method can be integrated with MRI data if available, it is specifically designed to be highly effective even in the absence of MRI, thus expanding the potential for advanced EEG analysis in various resource-limited settings. Our solution implements a two-tiered approach involving landmark/fiducials localization and electrode localization, creating an end-to-end framework. Main results. The efficacy and robustness of our approach have been validated on an extensive dataset containing over 400 3D scans from 278 subjects. The framework identifies pre-auricular points and achieves correct electrode positioning accuracy in the range of 85.7% to 91.0%. Additionally, our framework includes a validation tool that permits manual adjustments and visual validation if required. Significance. This study represents, to the best of the authors' knowledge, the first validation of such a method on a substantial dataset, thus ensuring the robustness and generalizability of our innovative approach. Our findings focus on developing a solution that facilitates source localization, without the need for MRI, contributing to the critical discussion on balancing cost effectiveness with methodological accuracy to promote wider adoption in both research and clinical settings. (Creative Commons Attribution license.)
Databáze:	MEDLINE
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