RT Conference Proceedings
T1 A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients
A1 Sarasola-Sanz, Andrea
A1 Irastorza-Landa, Nerea
A1 López-Larraz, Eduardo
A1 Bibián, Carlos
A1 Helmhold, Florian
A1 Broetz, Doris
A1 Birbaumer, Niels
A1 Ramos-Murguialday, Ander
A2 Ajoudani, Arash
A2 Artemiadis, Panagiotis
A2 Beckerle, Philipp
A2 Grioli, Giorgio
A2 Lambercy, Olivier
A2 Mombaur, Katja
A2 Novak, Domen
A2 Rauter, Georg
A2 Rodriguez Guerrero, Carlos
A2 Salvietti, Gionata
A2 Amirabdollahian, Farshid
A2 Balasubramanian, Sivakumar
A2 Castellini, Claudio
A2 Di Pino, Giovanni
A2 Guo, Zhao
A2 Hughes, Charmayne
A2 Iida, Fumiya
A2 Lenzi, Tommaso
A2 Ruffaldi, Emanuele
A2 Sergi, Fabrizio
A2 Soh, Gim Song
A2 Caimmi, Marco
A2 Cappello, Leonardo
A2 Carloni, Raffaella
A2 Carlson, Tom
A2 Casadio, Maura
A2 Coscia, Martina
A2 De Santis, Dalia
A2 Forner-Cordero, Arturo
A2 Howard, Matthew
A2 Piovesan, Davide
A2 Siqueira, Adriano
A2 Sup, Frank
A2 Lorenzo, Masia
A2 Catalano, Manuel Giuseppe
A2 Lee, Hyunglae
A2 Menon, Carlo
A2 Raspopovic, Stanisa
A2 Rastgaar, Mo
A2 Ronsse, Renaud
A2 van Asseldonk, Edwin
A2 Vanderborght, Bram
A2 Venkadesan, Madhusudhan
A2 Bianchi, Matteo
A2 Braun, David
A2 Godfrey, Sasha Blue
A2 Mastrogiovanni, Fulvio
A2 McDaid, Andrew
A2 Rossi, Stefano
A2 Zenzeri, Jacopo
A2 Formica, Domenico
A2 Karavas, Nikolaos
A2 Marchal-Crespo, Laura
A2 Reed, Kyle B.
A2 Tagliamonte, Nevio Luigi
A2 Burdet, Etienne
A2 Basteris, Angelo
A2 Campolo, Domenico
A2 Deshpande, Ashish
A2 Dubey, Venketesh
A2 Hussain, Asif
A2 Sanguineti, Vittorio
A2 Unal, Ramazan
A2 Caurin, Glauco Augusto de Paula
A2 Koike, Yasuharu
A2 Mazzoleni, Stefano
A2 Park, Hyung-Soon
A2 Remy, C. David
A2 Saint-Bauzel, Ludovic
A2 Tsagarakis, Nikos
A2 Veneman, Jan
A2 Zhang, Wenlong
AB 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.
PB IEEE Computer Society
SN 9781538622964
SN 1945-7898
YR 2017
FD 2017-08-11
LK https://hdl.handle.net/11556/2346
UL https://hdl.handle.net/11556/2346
LA eng
NO 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
NO conference
NO Publisher Copyright: © 2017 IEEE.
NO This study was funded by the Baden-Württemberg Stiftung (GRUENS ROB-1), the Deutsche Forschungsgemeinschaft (DFG, Koselleck), the Fortüne-Program of the University of Tübingen (2422-0-0), and the Bundes Ministerium für Bil-dung und Forschung BMBF MOTORBIC (FKZ 13GW0053) and AMORSA (FKZ 16SV7754). A. Sarasola-Sanz’s work is supported by La Caixa-DAAD and N. Irastorza-Landa’s work by the Basque Government and IKERBASQUE.
DS TECNALIA Publications
RD 26 jul 2024