ArticleExperimental brain research2021
Low-cost brain computer interface for everyday use.
Article in Experimental brain research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
Who cites it
4 citing papers in PubMed.
- Neural vs Neuromorphic Interfaces: Where Are We Standing?Chemical reviews · 2025Review
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Brain-computer interface: trend, challenges, and threats.Brain informatics · 2023Review
- The present and future of neural interfaces.Frontiers in neurorobotics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the growth in electroencephalography (EEG) based applications the demand for affordable consumer solutions is increasing. Here we describe a compact, low-cost EEG device suitable for daily use. The data are transferred from the device to a personal server using the TCP-IP protocol, allowing for wireless operation and a decent range of motion for the user. The device is compact, having a circular shape with a radius of only 25 mm, which would allow for comfortable daily use during both daytime and nighttime. Our solution is also very cost effective, approximately $350 for 24 electrodes. The built-in noise suppression capability improves the accuracy of recordings with a peak input noise below 0.35 μV. Here, we provide the results of the tests for the developed device. On our GitHub page, we provide detailed specification of the steps involved in building this EEG device which should be helpful to readers designing similar devices for their needs https://github.com/Ildaron/ironbci .
Indexed as
Identifiers
34586477What OpenQuestion holds
Registered trials
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.