A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients
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2017-08-11
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IEEE Computer Society
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Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user. Whereas most of the existing non-invasive EEG-EMG-hBMIs have only been subjected to offline testings or are limited to one degree of freedom (DoF), we present an EEG-EMG-hBMI that allows the simultaneous control of 7-DoFs of the upper limb with a robotic exoskeleton. Moreover, it establishes a biologically-inspired hierarchical control flow, requiring the active participation of central and peripheral structures of the nervous system. Contingent visual and proprioceptive feedback about the user's EEG and EMG activity is provided in the form of velocity modulation during functional task training. We believe that training with this closed-loop system may facilitate functional neuroplastic processes and eventually elicit a joint brain and muscle motor rehabilitation. Its usability is validated during a real-time operation session in a healthy participant and a chronic stroke patient, showing encouraging results for its application to a clinical rehabilitation scenario.
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Publisher Copyright: © 2017 IEEE.
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Sarasola-Sanz , A , Irastorza-Landa , N , López-Larraz , E , Bibián , C , Helmhold , F , Broetz , D , Birbaumer , N & Ramos-Murguialday , A 2017 , A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients . in A Ajoudani , P Artemiadis , P Beckerle , G Grioli , O Lambercy , K Mombaur , D Novak , G Rauter , C Rodriguez Guerrero , G Salvietti , F Amirabdollahian , S Balasubramanian , C Castellini , G Di Pino , Z Guo , C Hughes , F Iida , T Lenzi , E Ruffaldi , F Sergi , G S Soh , M Caimmi , L Cappello , R Carloni , T Carlson , M Casadio , M Coscia , D De Santis , A Forner-Cordero , M Howard , D Piovesan , A Siqueira , F Sup , M Lorenzo , M G Catalano , H Lee , C Menon , S Raspopovic , M Rastgaar , R Ronsse , E van Asseldonk , B Vanderborght , M Venkadesan , M Bianchi , D Braun , S B Godfrey , F Mastrogiovanni , A McDaid , S Rossi , J Zenzeri , D Formica , N Karavas , L Marchal-Crespo , K B Reed , N L Tagliamonte , E Burdet , A Basteris , D Campolo , A Deshpande , V Dubey , A Hussain , V Sanguineti , R Unal , G A D P Caurin , Y Koike , S Mazzoleni , H-S Park , C D Remy , L Saint-Bauzel , N Tsagarakis , J Veneman & W Zhang (eds) , 2017 International Conference on Rehabilitation Robotics, ICORR 2017 . , 8009362 , IEEE International Conference on Rehabilitation Robotics , IEEE Computer Society , pp. 895-900 , 2017 International Conference on Rehabilitation Robotics, ICORR 2017 , London , United Kingdom , 17/07/17 . https://doi.org/10.1109/ICORR.2017.8009362
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