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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Vydané v:Frontiers in neuroinformatics Ročník 11; s. 45
Hlavní autori: Rodríguez-Ugarte, Marisol, Iáñez, Eduardo, Ortíz, Mario, Azorín, Jose M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Research Foundation 11.07.2017
Frontiers Media S.A
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Edited by: Jose Manuel Ferrandez, Universidad Politécnica de Cartagena, Spain
Reviewed by: Monzurul Alam, Hong Kong Polytechnic University, Hong Kong; Miguel Almonacid, Universidad Politécnica de Cartagena, Spain
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2017.00045