Development of a closed-loop BMI for elbow movement assistance based on kinematical decoding

  • E. Y. VeslinEmail author
  • M. S. Dutra
  • L. Bevilacqua
  • L. S. C. Raptopoulos
  • W. S. Andrade
  • J. G. M. Soares
Technical Paper


A closed-loop brain–machine interface for elbow assistance is proposed in this work. The system decodes flexion and extension movements from noninvasive electroencephalographic signals through Kalman filter and uses them to activate a virtual device. A two-degree-of-freedom control scheme drives the model through a decoded path by generating a set of estimated inputs using differential flatness. These inputs are compensated by a feedback loop when decoding errors or external forces actuate the model. The results provide us an insight into the control architecture performance, which is dependent on the decoding precision. These decoding capabilities can be manipulated through a set of parameter configurations, enhancing the path tracking, or decreasing the decoding error according to their values.


Electroencephalography Kalman filter Brain–machine interfaces Decoding Upper member Differential flatness 



The authors would like to thank FINEP, CNPq, FAPERJ, Fundação COPPETEC and DIPPG/CEFET-RJ for supporting our work as well as the students Edwiges Beatriz Coimbra de Souza, Aline Macedo Rocha Rodriguez e André Silva for helping in the EEG data acquisition and Marco Vinicio Chiorri for technical assistance.


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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  1. 1.Robotics Laboratory, Department of Mechanical EngineeringFederal University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Department of Civil EngineeringFederal University of Rio de JaneiroRio de JaneiroBrazil
  3. 3.Federal Center of Technological Education Celso Suckow da Fonseca – CEFET/RJNova IguaçuBrazil
  4. 4.Laboratory of Cognitive Physiology, Institute of Biophysics Carlos Chagas FilhoFederal University of Rio de JaneiroRio de JaneiroBrazil
  5. 5.Rio de JaneiroBrazil

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