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Low-cost brain computer interface for everyday use

Authors:
Ildar Rakhmatulin at Heriot-Watt University
Ildar Rakhmatulin
Mikhail A Lebedev at National Research University Higher School of Economics
Mikhail A Lebedev
Zachary Traylor at North Carolina State University
Zachary Traylor
Andrey Parfenov
Andrey Parfenov
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Abstract and Figures

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.
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Experimental Brain Research (2021) 239:3573–3583
https://doi.org/10.1007/s00221-021-06231-4
RESEARCH ARTICLE
Low‑cost brain computer interface foreveryday use
IldarRakhmatulin1 · AndreyParfenov2· ZacharyTraylor3· ChangS.Nam3· MikhailLebedev4
Received: 27 June 2021 / Accepted: 21 September 2021 / Published online: 29 September 2021
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
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/ Ildar on/ ironb ci.
Keywords Brain computer interface (BCI)· EEG· Signal processing· ironbci· Low-cost BCI· Low-cost EEG
Introduction
Electroencephalography (EEG) is one of the most popular
methods for researching the brain; it functions via recordings
of neural activity using the electrodes placed on a subject’s
scalp. The information obtained via EEG is used for vari-
ous purposes, including research and clinical applications.
Many manuscripts in the field of EEG signal processing
even attempt to diagnose diseases with the use of informa-
tion received from EEG electrodes. For example, Kanda
etal. (2017) used the alpha rhythm of the brain (7–13Hz
signals) to improve the discrimination of mild Alzheimer’s
disease. Tylová etal. (2018) used an unbiased estimation of
permutation entropy for EEG analysis. Yu etal. (2018) used
the permutation disalignment index to research the func-
tional brain connectivity in Alzheimer’s disease patients.
More recently, Asadzadeh etal. (2020) presented a com-
plete review of brain pathologies based on EEG signals and
source localization.
Various types of electrode sensors can be used to “meas-
ure EEG signals, including but not limited to wet, contact,
and non-contact”. The highest quality signal is usually
obtained with disposable wet sensors due to their low imped-
ance: in some systems as high as 200 kOhm before applying
the “wet” conductive gel and as low as 5 kOhm after apply-
ing the gel (Lopez-Gordo 2014). One disadvantage of using
wet electrodes, however, is the impedance drift as the gel
dries, which introduces significant changes in the measured
signal. The usage of electrode gel also requires a great deal
of set-up time, and results in the patient needing to wash
the gel out of their hair after the experiment. Obtaining an
EEG signal with a dry contact electrode on the other hand
is a more comfortable way to obtain information about the
functioning of the human brain, as it avoids the mess of the
electrode gel and the set-up/take-down time associated with
cleaning the electrodes and the participant. Unfortunately,
this method is not the most reliable and not suitable for all
scenarios. A relatively high-contact impedance, due to insuf-
ficient electrical connection at the electrode–scalp interface,
Communicated by Bill J Yates.
* Ildar Rakhmatulin
ildar.o2010@yandex.ru
1 South Ural State University, Chelyabinsk, Russia
2 Brainflow, Moscow, Russia
3 Edward P. Fitts Department ofIndustrial andSystems
Engineering, North Carolina State University, Raleigh, NC,
USA
4 Skolkovo Institute ofScience andTechnology, Moscow,
Russia
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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Brain-Computer Interfaces for Communication and Motor Control—Perspectives on Clinical Applications
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  • Jul 2007
The latest research in the development of technologies that will allow humans to communicate, using brain signals only, with computers, wheelchairs, prostheses, and other devices. Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world.
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  • J NEUROSCI METH
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