A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients

Andrea Sarasola-Sanz; Nerea Irastorza-Landa; Eduardo López-Larraz; Carlos Bibián; Florian Helmhold; Doris Broetz; Niels Birbaumer; Ander Ramos-Murguialday

doi:10.1109/ICORR.2017.8009362

A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients

Andrea Sarasola-Sanz^*, Nerea Irastorza-Landa, Eduardo López-Larraz, Carlos Bibián, Florian Helmhold, Doris Broetz, Niels Birbaumer, Ander Ramos-Murguialday

^*Autor correspondiente de este trabajo

Medical Technologies

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

63 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	2017 International Conference on Rehabilitation Robotics, ICORR 2017
Editores	Arash Ajoudani, Panagiotis Artemiadis, Philipp Beckerle, Giorgio Grioli, Olivier Lambercy, Katja Mombaur, Domen Novak, Georg Rauter, Carlos Rodriguez Guerrero, Gionata Salvietti, Farshid Amirabdollahian, Sivakumar Balasubramanian, Claudio Castellini, Giovanni Di Pino, Zhao Guo, Charmayne Hughes, Fumiya Iida, Tommaso Lenzi, Emanuele Ruffaldi, Fabrizio Sergi, Gim Song Soh, Marco Caimmi, Leonardo Cappello, Raffaella Carloni, Tom Carlson, Maura Casadio, Martina Coscia, Dalia De Santis, Arturo Forner-Cordero, Matthew Howard, Davide Piovesan, Adriano Siqueira, Frank Sup, Masia Lorenzo, Manuel Giuseppe Catalano, Hyunglae Lee, Carlo Menon, Stanisa Raspopovic, Mo Rastgaar, Renaud Ronsse, Edwin van Asseldonk, Bram Vanderborght, Madhusudhan Venkadesan, Matteo Bianchi, David Braun, Sasha Blue Godfrey, Fulvio Mastrogiovanni, Andrew McDaid, Stefano Rossi, Jacopo Zenzeri, Domenico Formica, Nikolaos Karavas, Laura Marchal-Crespo, Kyle B. Reed, Nevio Luigi Tagliamonte, Etienne Burdet, Angelo Basteris, Domenico Campolo, Ashish Deshpande, Venketesh Dubey, Asif Hussain, Vittorio Sanguineti, Ramazan Unal, Glauco Augusto de Paula Caurin, Yasuharu Koike, Stefano Mazzoleni, Hyung-Soon Park, C. David Remy, Ludovic Saint-Bauzel, Nikos Tsagarakis, Jan Veneman, Wenlong Zhang
Editorial	IEEE Computer Society
Páginas	895-900
Número de páginas	6
ISBN (versión digital)	9781538622964
DOI	https://doi.org/10.1109/ICORR.2017.8009362
Estado	Publicada - 11 ago 2017
Evento	2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, Reino Unido Duración: 17 jul 2017 → 20 jul 2017

Serie de la publicación

Nombre	IEEE International Conference on Rehabilitation Robotics
ISSN (versión impresa)	1945-7898
ISSN (versión digital)	1945-7901

Conferencia

Conferencia	2017 International Conference on Rehabilitation Robotics, ICORR 2017
País/Territorio	Reino Unido
Ciudad	London
Período	17/07/17 → 20/07/17

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10.1109/ICORR.2017.8009362

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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. En 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 (pp. 895-900). Artículo 8009362 (IEEE International Conference on Rehabilitation Robotics). IEEE Computer Society. https://doi.org/10.1109/ICORR.2017.8009362

Sarasola-Sanz, Andrea ; Irastorza-Landa, Nerea ; López-Larraz, Eduardo et al. / A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients. 2017 International Conference on Rehabilitation Robotics, ICORR 2017. editor / Arash Ajoudani ; Panagiotis Artemiadis ; Philipp Beckerle ; Giorgio Grioli ; Olivier Lambercy ; Katja Mombaur ; Domen Novak ; Georg Rauter ; Carlos Rodriguez Guerrero ; Gionata Salvietti ; Farshid Amirabdollahian ; Sivakumar Balasubramanian ; Claudio Castellini ; Giovanni Di Pino ; Zhao Guo ; Charmayne Hughes ; Fumiya Iida ; Tommaso Lenzi ; Emanuele Ruffaldi ; Fabrizio Sergi ; Gim Song Soh ; Marco Caimmi ; Leonardo Cappello ; Raffaella Carloni ; Tom Carlson ; Maura Casadio ; Martina Coscia ; Dalia De Santis ; Arturo Forner-Cordero ; Matthew Howard ; Davide Piovesan ; Adriano Siqueira ; Frank Sup ; Masia Lorenzo ; Manuel Giuseppe Catalano ; Hyunglae Lee ; Carlo Menon ; Stanisa Raspopovic ; Mo Rastgaar ; Renaud Ronsse ; Edwin van Asseldonk ; Bram Vanderborght ; Madhusudhan Venkadesan ; Matteo Bianchi ; David Braun ; Sasha Blue Godfrey ; Fulvio Mastrogiovanni ; Andrew McDaid ; Stefano Rossi ; Jacopo Zenzeri ; Domenico Formica ; Nikolaos Karavas ; Laura Marchal-Crespo ; Kyle B. Reed ; Nevio Luigi Tagliamonte ; Etienne Burdet ; Angelo Basteris ; Domenico Campolo ; Ashish Deshpande ; Venketesh Dubey ; Asif Hussain ; Vittorio Sanguineti ; Ramazan Unal ; Glauco Augusto de Paula Caurin ; Yasuharu Koike ; Stefano Mazzoleni ; Hyung-Soon Park ; C. David Remy ; Ludovic Saint-Bauzel ; Nikos Tsagarakis ; Jan Veneman ; Wenlong Zhang. IEEE Computer Society, 2017. pp. 895-900 (IEEE International Conference on Rehabilitation Robotics).

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title = "A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients",

abstract = "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.",

author = "Andrea Sarasola-Sanz and Nerea Irastorza-Landa and Eduardo L{\'o}pez-Larraz and Carlos Bibi{\'a}n and Florian Helmhold and Doris Broetz and Niels Birbaumer and Ander Ramos-Murguialday",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Rehabilitation Robotics, ICORR 2017 ; Conference date: 17-07-2017 Through 20-07-2017",

year = "2017",

month = aug,

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doi = "10.1109/ICORR.2017.8009362",

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editor = "Arash Ajoudani and Panagiotis Artemiadis and Philipp Beckerle and Giorgio Grioli and Olivier Lambercy and Katja Mombaur and Domen Novak and Georg Rauter and {Rodriguez Guerrero}, Carlos and Gionata Salvietti and Farshid Amirabdollahian and Sivakumar Balasubramanian and Claudio Castellini and {Di Pino}, Giovanni and Zhao Guo and Charmayne Hughes and Fumiya Iida and Tommaso Lenzi and Emanuele Ruffaldi and Fabrizio Sergi and Soh, {Gim Song} and Marco Caimmi and Leonardo Cappello and Raffaella Carloni and Tom Carlson and Maura Casadio and Martina Coscia and {De Santis}, Dalia and Arturo Forner-Cordero and Matthew Howard and Davide Piovesan and Adriano Siqueira and Frank Sup and Masia Lorenzo and Catalano, {Manuel Giuseppe} and Hyunglae Lee and Carlo Menon and Stanisa Raspopovic and Mo Rastgaar and Renaud Ronsse and {van Asseldonk}, Edwin and Bram Vanderborght and Madhusudhan Venkadesan and Matteo Bianchi and David Braun and Godfrey, {Sasha Blue} and Fulvio Mastrogiovanni and Andrew McDaid and Stefano Rossi and Jacopo Zenzeri and Domenico Formica and Nikolaos Karavas and Laura Marchal-Crespo and Reed, {Kyle B.} and Tagliamonte, {Nevio Luigi} and Etienne Burdet and Angelo Basteris and Domenico Campolo and Ashish Deshpande and Venketesh Dubey and Asif Hussain and Vittorio Sanguineti and Ramazan Unal and Caurin, {Glauco Augusto de Paula} and Yasuharu Koike and Stefano Mazzoleni and Hyung-Soon Park and Remy, {C. David} and Ludovic Saint-Bauzel and Nikos Tsagarakis and Jan Veneman and Wenlong Zhang",

booktitle = "2017 International Conference on Rehabilitation Robotics, ICORR 2017",

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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. En 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, GS 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, MG Catalano, H Lee, C Menon, S Raspopovic, M Rastgaar, R Ronsse, E van Asseldonk, B Vanderborght, M Venkadesan, M Bianchi, D Braun, SB Godfrey, F Mastrogiovanni, A McDaid, S Rossi, J Zenzeri, D Formica, N Karavas, L Marchal-Crespo, KB Reed, NL Tagliamonte, E Burdet, A Basteris, D Campolo, A Deshpande, V Dubey, A Hussain, V Sanguineti, R Unal, GADP Caurin, Y Koike, S Mazzoleni, H-S Park, CD 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, Reino Unido, 17/07/17. https://doi.org/10.1109/ICORR.2017.8009362

A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients. / Sarasola-Sanz, Andrea ; Irastorza-Landa, Nerea; López-Larraz, Eduardo et al.
2017 International Conference on Rehabilitation Robotics, ICORR 2017. ed. / Arash Ajoudani; Panagiotis Artemiadis; Philipp Beckerle; Giorgio Grioli; Olivier Lambercy; Katja Mombaur; Domen Novak; Georg Rauter; Carlos Rodriguez Guerrero; Gionata Salvietti; Farshid Amirabdollahian; Sivakumar Balasubramanian; Claudio Castellini; Giovanni Di Pino; Zhao Guo; Charmayne Hughes; Fumiya Iida; Tommaso Lenzi; Emanuele Ruffaldi; Fabrizio Sergi; Gim Song Soh; Marco Caimmi; Leonardo Cappello; Raffaella Carloni; Tom Carlson; Maura Casadio; Martina Coscia; Dalia De Santis; Arturo Forner-Cordero; Matthew Howard; Davide Piovesan; Adriano Siqueira; Frank Sup; Masia Lorenzo; Manuel Giuseppe Catalano; Hyunglae Lee; Carlo Menon; Stanisa Raspopovic; Mo Rastgaar; Renaud Ronsse; Edwin van Asseldonk; Bram Vanderborght; Madhusudhan Venkadesan; Matteo Bianchi; David Braun; Sasha Blue Godfrey; Fulvio Mastrogiovanni; Andrew McDaid; Stefano Rossi; Jacopo Zenzeri; Domenico Formica; Nikolaos Karavas; Laura Marchal-Crespo; Kyle B. Reed; Nevio Luigi Tagliamonte; Etienne Burdet; Angelo Basteris; Domenico Campolo; Ashish Deshpande; Venketesh Dubey; Asif Hussain; Vittorio Sanguineti; Ramazan Unal; Glauco Augusto de Paula Caurin; Yasuharu Koike; Stefano Mazzoleni; Hyung-Soon Park; C. David Remy; Ludovic Saint-Bauzel; Nikos Tsagarakis; Jan Veneman; Wenlong Zhang. IEEE Computer Society, 2017. p. 895-900 8009362 (IEEE International Conference on Rehabilitation Robotics).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients

AU - Sarasola-Sanz, Andrea

AU - Irastorza-Landa, Nerea

AU - López-Larraz, Eduardo

AU - Bibián, Carlos

AU - Helmhold, Florian

AU - Broetz, Doris

AU - Birbaumer, Niels

AU - Ramos-Murguialday, Ander

PY - 2017/8/11

Y1 - 2017/8/11

N2 - 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.

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.

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U2 - 10.1109/ICORR.2017.8009362

DO - 10.1109/ICORR.2017.8009362

M3 - Conference contribution

C2 - 28813934

AN - SCOPUS:85032185913

T3 - IEEE International Conference on Rehabilitation Robotics

SP - 895

EP - 900

BT - 2017 International Conference on Rehabilitation Robotics, ICORR 2017

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

PB - IEEE Computer Society

T2 - 2017 International Conference on Rehabilitation Robotics, ICORR 2017

Y2 - 17 July 2017 through 20 July 2017

ER -

Sarasola-Sanz A , Irastorza-Landa N, López-Larraz E, Bibián C, Helmhold F, Broetz D et al. A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients. En Ajoudani A, Artemiadis P, Beckerle P, Grioli G, Lambercy O, Mombaur K, Novak D, Rauter G, Rodriguez Guerrero C, Salvietti G, Amirabdollahian F, Balasubramanian S, Castellini C, Di Pino G, Guo Z, Hughes C, Iida F, Lenzi T, Ruffaldi E, Sergi F, Soh GS, Caimmi M, Cappello L, Carloni R, Carlson T, Casadio M, Coscia M, De Santis D, Forner-Cordero A, Howard M, Piovesan D, Siqueira A, Sup F, Lorenzo M, Catalano MG, Lee H, Menon C, Raspopovic S, Rastgaar M, Ronsse R, van Asseldonk E, Vanderborght B, Venkadesan M, Bianchi M, Braun D, Godfrey SB, Mastrogiovanni F, McDaid A, Rossi S, Zenzeri J, Formica D, Karavas N, Marchal-Crespo L, Reed KB, Tagliamonte NL, Burdet E, Basteris A, Campolo D, Deshpande A, Dubey V, Hussain A, Sanguineti V, Unal R, Caurin GADP, Koike Y, Mazzoleni S, Park HS, Remy CD, Saint-Bauzel L, Tsagarakis N, Veneman J, Zhang W, editores, 2017 International Conference on Rehabilitation Robotics, ICORR 2017. IEEE Computer Society. 2017. p. 895-900. 8009362. (IEEE International Conference on Rehabilitation Robotics). doi: 10.1109/ICORR.2017.8009362

A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients

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