Evidence map›Paper›PMID 37299902›Full record

ArticleSensors (Basel, Switzerland)2023

Fully 3D-Printed Dry EEG Electrodes.

Adele Tong, Praneeth Perera, Zhanna Sarsenbayeva, Alistair McEwan, Anjula C De Silva, Anusha Withana

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Adele TongSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0009-0001-9989-8097
Praneeth PereraSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0001-9390-8381
Zhanna SarsenbayevaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.
Alistair McEwanSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0001-7597-6372
Anjula C De SilvaDepartment of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.ORCID 0000-0001-8685-2076
Anusha WithanaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia.

Funding

Australian Research Council Discovery Early Career Award DE200100479Neurodisability Assist Trust, Australia PRG04219
6 · The paper itself

Abstract

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.

Indexed as

ElectroencephalographyScalpBrainElectrodesHumansPrinting, Three-Dimensional3D printingconductive filamentdry electrodesEEG

Identifiers

PMID37299902
PMCPMC10255664

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.