Fully 3D-Printed Dry EEG Electrodes.


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
29 May 2023
Historique:
received: 06 05 2023
revised: 24 05 2023
accepted: 24 05 2023
medline: 12 6 2023
pubmed: 10 6 2023
entrez: 10 6 2023
Statut: epublish

Résumé

Electroencephalography (EEG) is used to detect brain activity by recording electrical signals across various points on the scalp. Recent technological advancement has allowed brain signals to be monitored continuously through the long-term usage of EEG wearables. However, current EEG electrodes are not able to cater to different anatomical features, lifestyles, and personal preferences, suggesting the need for customisable electrodes. Despite previous efforts to create customisable EEG electrodes through 3D printing, additional processing after printing is often needed to achieve the required electrical properties. Although fabricating EEG electrodes entirely through 3D printing with a conductive material would eliminate the need for further processing, fully 3D-printed EEG electrodes have not been seen in previous studies. In this study, we investigate the feasibility of using a low-cost setup and a conductive filament, Multi3D Electrifi, to 3D print EEG electrodes. Our results show that the contact impedance between the printed electrodes and an artificial phantom scalp is under 550 Ω, with phase change of smaller than -30∘, for all design configurations for frequencies ranging from 20 Hz to 10 kHz. In addition, the difference in contact impedance between electrodes with different numbers of pins is under 200 Ω for all test frequencies. Through a preliminary functional test that monitored the alpha signals (7-13 Hz) of a participant in eye-open and eye-closed states, we show that alpha activity can be identified using the printed electrodes. This work demonstrates that fully 3D-printed electrodes have the capability of acquiring relatively high-quality EEG signals.

Identifiants

pubmed: 37299902
pii: s23115175
doi: 10.3390/s23115175
pmc: PMC10255664
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Australian Research Council Discovery Early Career Award
ID : DE200100479
Organisme : Neurodisability Assist Trust, Australia
ID : PRG04219

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Auteurs

Adele Tong (A)

School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.

Praneeth Perera (P)

School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.
Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.

Zhanna Sarsenbayeva (Z)

School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.

Alistair McEwan (A)

School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.

Anjula C De Silva (AC)

Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.

Anusha Withana (A)

School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.
Sydney Nano, The University of Sydney, Sydney, NSW 2006, Australia.

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Classifications MeSH