Literature DB >> 21048286

Brain-computer interface using water-based electrodes.

Ivan Volosyak1, Diana Valbuena, Tatsiana Malechka, Jan Peuscher, Axel Gräser.   

Abstract

Current brain-computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the standard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min(-1) and of 26.56 ± 9.224 bit min(-1), respectively. A paired t-test failed to reveal significant differences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI applications.

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Year:  2010        PMID: 21048286     DOI: 10.1088/1741-2560/7/6/066007

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  9 in total

1.  Comparison of dry and gel based electrodes for p300 brain-computer interfaces.

Authors:  Christoph Guger; Gunther Krausz; Brendan Z Allison; Guenter Edlinger
Journal:  Front Neurosci       Date:  2012-05-07       Impact factor: 4.677

2.  Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain-Computer Interface: Case Studies.

Authors:  Andreas Pinegger; Selina C Wriessnegger; Josef Faller; Gernot R Müller-Putz
Journal:  Front Neurosci       Date:  2016-09-30       Impact factor: 4.677

3.  Proprioceptive Feedback Facilitates Motor Imagery-Related Operant Learning of Sensorimotor β-Band Modulation.

Authors:  Sam Darvishi; Alireza Gharabaghi; Chadwick B Boulay; Michael C Ridding; Derek Abbott; Mathias Baumert
Journal:  Front Neurosci       Date:  2017-02-09       Impact factor: 4.677

Review 4.  Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

Authors:  Aleksandra Kawala-Sterniuk; Natalia Browarska; Amir Al-Bakri; Mariusz Pelc; Jaroslaw Zygarlicki; Michaela Sidikova; Radek Martinek; Edward Jacek Gorzelanczyk
Journal:  Brain Sci       Date:  2021-01-03

5.  Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.

Authors:  Nicolina Sciaraffa; Gianluca Di Flumeri; Daniele Germano; Andrea Giorgi; Antonio Di Florio; Gianluca Borghini; Alessia Vozzi; Vincenzo Ronca; Fabio Babiloni; Pietro Aricò
Journal:  Front Hum Neurosci       Date:  2022-07-14       Impact factor: 3.473

6.  Noise reduction in brainwaves by using both EEG signals and frontal viewing camera images.

Authors:  Jae Won Bang; Jong-Suk Choi; Kang Ryoung Park
Journal:  Sensors (Basel)       Date:  2013-05-13       Impact factor: 3.576

7.  Textile electrodes for EEG recording--a pilot study.

Authors:  Johan Löfhede; Fernando Seoane; Magnus Thordstein
Journal:  Sensors (Basel)       Date:  2012-12-07       Impact factor: 3.576

8.  Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.

Authors:  Domen Novak; Roland Sigrist; Nicolas J Gerig; Dario Wyss; René Bauer; Ulrich Götz; Robert Riener
Journal:  Front Neurosci       Date:  2018-01-11       Impact factor: 4.677

Review 9.  Brain-Computer Interface Spellers: A Review.

Authors:  Aya Rezeika; Mihaly Benda; Piotr Stawicki; Felix Gembler; Abdul Saboor; Ivan Volosyak
Journal:  Brain Sci       Date:  2018-03-30
  9 in total

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