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Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces
Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user’s intentions. In this work, we have designed two different approaches ba...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004835/ https://www.ncbi.nlm.nih.gov/pubmed/33810122 http://dx.doi.org/10.3390/s21062220 |
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author | Laport, Francisco Iglesia, Daniel Dapena, Adriana Castro, Paula M. Vazquez-Araujo, Francisco J. |
author_facet | Laport, Francisco Iglesia, Daniel Dapena, Adriana Castro, Paula M. Vazquez-Araujo, Francisco J. |
author_sort | Laport, Francisco |
collection | PubMed |
description | Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user’s intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms—one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements. |
format | Online Article Text |
id | pubmed-8004835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80048352021-03-29 Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces Laport, Francisco Iglesia, Daniel Dapena, Adriana Castro, Paula M. Vazquez-Araujo, Francisco J. Sensors (Basel) Article Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user’s intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms—one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements. MDPI 2021-03-22 /pmc/articles/PMC8004835/ /pubmed/33810122 http://dx.doi.org/10.3390/s21062220 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Laport, Francisco Iglesia, Daniel Dapena, Adriana Castro, Paula M. Vazquez-Araujo, Francisco J. Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces |
title | Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces |
title_full | Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces |
title_fullStr | Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces |
title_full_unstemmed | Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces |
title_short | Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces |
title_sort | proposals and comparisons from one-sensor eeg and eog human-machine interfaces |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004835/ https://www.ncbi.nlm.nih.gov/pubmed/33810122 http://dx.doi.org/10.3390/s21062220 |
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