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...

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Published in:	Frontiers in neuroinformatics Vol. 11; p. 45
Main Authors:	Rodríguez-Ugarte, Marisol, Iáñez, Eduardo, Ortíz, Mario, Azorín, Jose M.
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Research Foundation 11.07.2017 Frontiers Media S.A
Subjects:	Algorithms EEG electrode configurations Electrodes Electroencephalography Exoskeleton feature extraction algorithms Gait Internet Neuroscience offline Patients pedaling intention personalized brain-computer interfaces pseudo-online Rehabilitation Stroke Walking New York Spain offline electrode configurations pedaling intention personalized brain-computer interfaces feature extraction algorithms pseudo-online
ISSN:	1662-5196, 1662-5196
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Abstract	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.
AbstractList	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. 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.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.
Author	Iáñez, Eduardo Ortíz, Mario Rodríguez-Ugarte, Marisol Azorín, Jose M.
AuthorAffiliation	Brain-Machine Interface Systems Lab, Systems Engineering and Automation Department, Miguel Hernández University of Elche Elche, Spain
AuthorAffiliation_xml	– name: Brain-Machine Interface Systems Lab, Systems Engineering and Automation Department, Miguel Hernández University of Elche Elche, Spain
Author_xml	– sequence: 1 givenname: Marisol surname: Rodríguez-Ugarte fullname: Rodríguez-Ugarte, Marisol – sequence: 2 givenname: Eduardo surname: Iáñez fullname: Iáñez, Eduardo – sequence: 3 givenname: Mario surname: Ortíz fullname: Ortíz, Mario – sequence: 4 givenname: Jose M. surname: Azorín fullname: Azorín, Jose M.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28744212$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1186/s12984-015-0087-4 10.1007/978-0-387-77242-4 10.1113/jphysiol.2006.123067 10.1109/embc.2016.7590993 10.1016/S0013-4694(97)00022-2 10.1016/j.clinph.2010.08.002 10.1109/TBME.2013.2294203 10.3389/fneng.2012.00013 10.1037/0735-7044.121.5.854 10.1186/1743-0003-11-153 10.1109/EMBC.2014.6944532 10.1109/TBME.2007.912653 10.1016/j.neuroimage.2005.12.003 10.1016/S0013-4694(98)00051-0 10.1016/j.clinph.2010.07.010 10.1186/1743-0003-9-65 10.1109/IEMBS.2010.5625947 10.3390/s141018172 10.1016/j.artmed.2013.07.004 10.1007/978-3-319-26242-0_10 10.1016/j.clinph.2007.08.025 10.1017/CBO9781139032803 10.3389/fnins.2014.00376 10.1016/S1474-4422(08)70223-0 10.1007/978-1-4615-0189-3 10.1016/0301-0511(77)90028-X 10.1016/j.clinph.2014.05.003 10.5626/JCSE.2013.7.2.139
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Copyright	2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2017 Rodríguez-Ugarte, Iáñez, Ortíz and Azorín. 2017 Rodríguez-Ugarte, Iáñez, Ortíz and Azorín
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Keywords	offline electrode configurations pedaling intention personalized brain-computer interfaces feature extraction algorithms pseudo-online
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Snippet	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...
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StartPage	45
SubjectTerms	Algorithms EEG electrode configurations Electrodes Electroencephalography Exoskeleton feature extraction algorithms Gait Internet Neuroscience offline Patients pedaling intention personalized brain-computer interfaces pseudo-online Rehabilitation Stroke Walking
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Title	Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent
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