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Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication
Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to l...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040089/ https://www.ncbi.nlm.nih.gov/pubmed/32132894 http://dx.doi.org/10.3389/fnins.2020.00105 |
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author | Abdalmalak, Androu Milej, Daniel Yip, Lawrence C. M. Khan, Ali R. Diop, Mamadou Owen, Adrian M. St. Lawrence, Keith |
author_facet | Abdalmalak, Androu Milej, Daniel Yip, Lawrence C. M. Khan, Ali R. Diop, Mamadou Owen, Adrian M. St. Lawrence, Keith |
author_sort | Abdalmalak, Androu |
collection | PubMed |
description | Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for “mental communication” on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for “yes” and to stay relaxed for “no.” The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as “yes” or “no” responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries. |
format | Online Article Text |
id | pubmed-7040089 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70400892020-03-04 Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication Abdalmalak, Androu Milej, Daniel Yip, Lawrence C. M. Khan, Ali R. Diop, Mamadou Owen, Adrian M. St. Lawrence, Keith Front Neurosci Neuroscience Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for “mental communication” on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for “yes” and to stay relaxed for “no.” The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as “yes” or “no” responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries. Frontiers Media S.A. 2020-02-18 /pmc/articles/PMC7040089/ /pubmed/32132894 http://dx.doi.org/10.3389/fnins.2020.00105 Text en Copyright © 2020 Abdalmalak, Milej, Yip, Khan, Diop, Owen and St. Lawrence. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Abdalmalak, Androu Milej, Daniel Yip, Lawrence C. M. Khan, Ali R. Diop, Mamadou Owen, Adrian M. St. Lawrence, Keith Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication |
title | Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication |
title_full | Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication |
title_fullStr | Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication |
title_full_unstemmed | Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication |
title_short | Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication |
title_sort | assessing time-resolved fnirs for brain-computer interface applications of mental communication |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040089/ https://www.ncbi.nlm.nih.gov/pubmed/32132894 http://dx.doi.org/10.3389/fnins.2020.00105 |
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