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dc.contributor.authorShiman, Farid
dc.contributor.authorLópez-Larraz, Eduardo
dc.contributor.authorSarasola-Sanz, Andrea
dc.contributor.authorIrastorza-Landa, Nerea
dc.contributor.authorSpüler, Martin
dc.contributor.authorBirbaumer, Niels
dc.contributor.authorRamos-Murguialday, Ander
dc.date.accessioned2017-11-07T13:09:58Z
dc.date.available2017-11-07T13:09:58Z
dc.date.issued2017
dc.identifier.citationShiman, Farid, Eduardo López-Larraz, Andrea Sarasola-Sanz, Nerea Irastorza-Landa, Martin Spüler, Niels Birbaumer, and Ander Ramos-Murguialday. “Classification of Different Reaching Movements from the Same Limb Using EEG.” Journal of Neural Engineering 14, no. 4 (June 12, 2017): 046018. doi:10.1088/1741-2552/aa70d2.en
dc.identifier.issn1741-2560en
dc.identifier.urihttp://hdl.handle.net/11556/444
dc.description.abstractObjective. Brain–computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.en
dc.description.sponsorshipThis study was funded by the Baden-Württemberg Stiftung (GRUENS), the Deutsche Forschungsgemeinschaft (DFG, Koselleck and SP-1533/2-1), Bundes Ministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053), the fortune-Program of the University of Tübingen (2422-0-0), and AMORSA (FKZ 16SV7754). A Sarasola-Sanz’s work is supported by the La Caixa-DAAD scholarship, and N IrastorzaLanda’s work by the Basque Government and IKERBASQUE, Basque Foundation for Science.en
dc.language.isoengen
dc.publisherIOP PUBLISHING LTD, TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLANDen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleClassification of different reaching movements from the same limb using EEGen
dc.typearticleen
dc.identifier.doi10.1088/1741-2552/aa70d2en
dc.isiYesen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsBrain-computer interface (BCI)en
dc.subject.keywordsElectroencephalography (EEG)en
dc.subject.keywordsMotor rehabilitationen
dc.identifier.essn1741-2552en
dc.issue.number4en
dc.journal.titleJournal of Neural Engineeringen
dc.volume.number14en


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