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On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems
Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472325/ https://www.ncbi.nlm.nih.gov/pubmed/32785025 http://dx.doi.org/10.3390/s20164452 |
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author | Riquelme-Ros, José-Vicente Rodríguez-Bermúdez, Germán Rodríguez-Rodríguez, Ignacio Rodríguez, José-Víctor Molina-García-Pardo, José-María |
author_facet | Riquelme-Ros, José-Vicente Rodríguez-Bermúdez, Germán Rodríguez-Rodríguez, Ignacio Rodríguez, José-Víctor Molina-García-Pardo, José-María |
author_sort | Riquelme-Ros, José-Vicente |
collection | PubMed |
description | Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems. |
format | Online Article Text |
id | pubmed-7472325 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74723252020-09-04 On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems Riquelme-Ros, José-Vicente Rodríguez-Bermúdez, Germán Rodríguez-Rodríguez, Ignacio Rodríguez, José-Víctor Molina-García-Pardo, José-María Sensors (Basel) Article Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems. MDPI 2020-08-10 /pmc/articles/PMC7472325/ /pubmed/32785025 http://dx.doi.org/10.3390/s20164452 Text en © 2020 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 Riquelme-Ros, José-Vicente Rodríguez-Bermúdez, Germán Rodríguez-Rodríguez, Ignacio Rodríguez, José-Víctor Molina-García-Pardo, José-María On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems |
title | On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems |
title_full | On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems |
title_fullStr | On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems |
title_full_unstemmed | On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems |
title_short | On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems |
title_sort | on the better performance of pianists with motor imagery-based brain-computer interface systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472325/ https://www.ncbi.nlm.nih.gov/pubmed/32785025 http://dx.doi.org/10.3390/s20164452 |
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