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• W2231596722 endingPage "e0146610" @default.
• W2231596722 startingPage "e0146610" @default.
• W2231596722 abstract "Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes." @default.
• W2231596722 created "2016-06-24" @default.
• W2231596722 creator A5022611002 @default.
• W2231596722 creator A5022670151 @default.
• W2231596722 creator A5060057902 @default.
• W2231596722 date "2016-01-05" @default.
• W2231596722 modified "2023-10-14" @default.
• W2231596722 title "Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks" @default.
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• W2231596722 doi "https://doi.org/10.1371/journal.pone.0146610" @default.
• W2231596722 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4701662" @default.
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• W2231596722 hasPublicationYear "2016" @default.
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