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Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent
The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode confi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504298/ https://www.ncbi.nlm.nih.gov/pubmed/28744212 http://dx.doi.org/10.3389/fninf.2017.00045 |
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author | Rodríguez-Ugarte, Marisol Iáñez, Eduardo Ortíz, Mario Azorín, Jose M. |
author_facet | Rodríguez-Ugarte, Marisol Iáñez, Eduardo Ortíz, Mario Azorín, Jose M. |
author_sort | Rodríguez-Ugarte, Marisol |
collection | PubMed |
description | The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and pseudo-online (in a way suitable for real-time applications), with a preference for the latter case. A process for selecting the best BCI model was described in detail. Results for the pseudo-online processing with the best BCI model of each subject were on average 76.7% of true positive rate, 4.94 false positives per minute and 55.1% of accuracy. The personalized BCI model approach was also found to be significantly advantageous when compared to the typical approach of using a fixed feature extraction algorithm and electrode configuration. The resulting approach could be used to more robustly interface with lower limb exoskeletons in the context of the rehabilitation of stroke patients. |
format | Online Article Text |
id | pubmed-5504298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55042982017-07-25 Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent Rodríguez-Ugarte, Marisol Iáñez, Eduardo Ortíz, Mario Azorín, Jose M. Front Neuroinform Neuroscience The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and pseudo-online (in a way suitable for real-time applications), with a preference for the latter case. A process for selecting the best BCI model was described in detail. Results for the pseudo-online processing with the best BCI model of each subject were on average 76.7% of true positive rate, 4.94 false positives per minute and 55.1% of accuracy. The personalized BCI model approach was also found to be significantly advantageous when compared to the typical approach of using a fixed feature extraction algorithm and electrode configuration. The resulting approach could be used to more robustly interface with lower limb exoskeletons in the context of the rehabilitation of stroke patients. Frontiers Media S.A. 2017-07-11 /pmc/articles/PMC5504298/ /pubmed/28744212 http://dx.doi.org/10.3389/fninf.2017.00045 Text en Copyright © 2017 Rodríguez-Ugarte, Iáñez, Ortíz and Azorín. 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) or licensor 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 Rodríguez-Ugarte, Marisol Iáñez, Eduardo Ortíz, Mario Azorín, Jose M. Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent |
title | Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent |
title_full | Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent |
title_fullStr | Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent |
title_full_unstemmed | Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent |
title_short | Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent |
title_sort | personalized offline and pseudo-online bci models to detect pedaling intent |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504298/ https://www.ncbi.nlm.nih.gov/pubmed/28744212 http://dx.doi.org/10.3389/fninf.2017.00045 |
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