Relevance-based Channel Selection for EEG Source Reconstruction: An Approach to Identify Low-density Channel Subsets

Andres Soler, Eduardo Giraldo, Lars Lundheim, Marta Molinas

2022

Abstract

Electroencephalography (EEG) Source Reconstruction is the estimation of the underlying neural activity at cortical areas. Currently, the most accurate estimations are done by combining the information registered by high-density sets of electrodes distributed over the scalp, with realistic head models that encode the morphology and conduction properties of different head tissues. However, the use of high-density EEG can be unpractical due to the large number of electrodes to set up, and it might not be required in all the EEG applications. In this study, we applied relevance criteria for selecting relevant channels to identify low-density subsets of electrodes that can be used to reconstruct the neural activity on given brain areas, while maintaining the reconstruction quality of a high-density system. We compare the performance of the proposed relevance-based selection with multiple high- and low-density montages based on standard montages and coverage during the reconstruction process of multiple sources and areas. We assessed several source reconstruction algorithms and concluded that the localization accuracy and waveform of reconstructed sources with subsets of 6 and 9 relevant channels can be comparable with reconstructions done with a distributed set of 128 channels, and better than 62 channels distributed in standard 10-10 positions.

Paper Citation

in Harvard Style

Soler A., Giraldo E., Lundheim L. and Molinas M. (2022). Relevance-based Channel Selection for EEG Source Reconstruction: An Approach to Identify Low-density Channel Subsets. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING; ISBN 978-989-758-552-4, SciTePress, pages 174-183. DOI: 10.5220/0010907100003123

in Bibtex Style

@conference{bioimaging22,
author={Andres Soler and Eduardo Giraldo and Lars Lundheim and Marta Molinas},
title={Relevance-based Channel Selection for EEG Source Reconstruction: An Approach to Identify Low-density Channel Subsets},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING},
year={2022},
pages={174-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010907100003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING
TI - Relevance-based Channel Selection for EEG Source Reconstruction: An Approach to Identify Low-density Channel Subsets
SN - 978-989-758-552-4
AU - Soler A.
AU - Giraldo E.
AU - Lundheim L.
AU - Molinas M.
PY - 2022
SP - 174
EP - 183
DO - 10.5220/0010907100003123
PB - SciTePress