Enhanced Automated Determination of Optimal Positions for Electroencephalography (EEG) Electrode Placements Using Data-Driven Hybrid Machine Learning (ML) Models-Reference-Cited by-同舟云学术

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Enhanced Automated Determination of Optimal Positions for Electroencephalography (EEG) Electrode Placements Using Data-Driven Hybrid Machine Learning (ML) Models

Published:2023-11-01 Issue:21 Volume:23 Page:26575-26586
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Souganttika M.¹^ORCID,Hussain Shaista²^ORCID,Foong Shaohui¹^ORCID

Affiliation:

1. Engineering Product Development (EPD) Pillar Singapore University of Technology and Design (SUTD), Singapore, Singapore

2. Institute of High-Performance Computing, A*STAR, Fusionopolis, Singapore

Funder

Ministry of Education - Singapore

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10302460/10247215.pdf?arnumber=10247215

Reference26 articles.

1. Smartphone-based photogrammetry provides improved localization and registration of scalp-mounted neuroimaging sensors

2. Accuracy of high-density EEG electrode position measurement using an optical scanner compared with the photogrammetry method

3. EEG SOURCE LOCALIZATION: A COMPARATIVE STUDY OF CLASSICAL AND NEURAL NETWORK METHODS

4. Review on Mathematical Modelling of Electroencephalography (EEG)

5. Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera

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