{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T03:09:18Z","timestamp":1777432158175,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain\u2013computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application (\u201cEaR-P Lab\u201d) that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects\u2019 ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.<\/jats:p>","DOI":"10.3390\/s24041226","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T03:57:29Z","timestamp":1707969449000},"page":"1226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4528-5634","authenticated-orcid":false,"given":"Guilherme","family":"Correia","sequence":"first","affiliation":[{"name":"Department of Physics, NOVA School of Science and Technology, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8282-8973","authenticated-orcid":false,"given":"Michael J.","family":"Crosse","sequence":"additional","affiliation":[{"name":"Segotia, H91 HE9E Galway, Ireland"},{"name":"Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5568-9808","authenticated-orcid":false,"given":"Alejandro","family":"Lopez Valdes","sequence":"additional","affiliation":[{"name":"Trinity Centre for Biomedical Engineering, Trinity College Dublin, D02 R590 Dublin, Ireland"},{"name":"Global Brain Health Institute, Trinity College Dublin, D02 X9W9 Dublin, Ireland"},{"name":"Trinity College Institute of Neuroscience, Trinity College Dublin, D02 X9W9 Dublin, Ireland"},{"name":"Department of Electronic and Electrical Engineering, Trinity College Dublin, D02 Dublin, Ireland"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MEMB.2010.936545","article-title":"Wearable Electroencephalography","volume":"29","author":"Casson","year":"2010","journal-title":"IEEE Eng. 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