EEG-based BCI for the linear control of an upper-limb neuroprosthesis

Carmen Vidaurre; Christian Klauer; Thomas Schauer; Ander Ramos-Murguialday; Klaus Robert Müller

doi:10.1016/j.medengphy.2016.06.010

EEG-based BCI for the linear control of an upper-limb neuroprosthesis

Carmen Vidaurre^*, Christian Klauer, Thomas Schauer, Ander Ramos-Murguialday, Klaus Robert Müller

^*Corresponding author for this work

Neuroengineering

Research output: Contribution to journal › Article › peer-review

50 Citations (Scopus)

Abstract

Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI–FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.

Original language	English
Pages (from-to)	1195-1204
Number of pages	10
Journal	Medical Engineering and Physics
Volume	38
Issue number	11
DOIs	https://doi.org/10.1016/j.medengphy.2016.06.010
Publication status	Published - 1 Nov 2016

Funding

The authors thankfully acknowledge Javier Pascual for his help and dedication. They are also grateful to the reviewers for their constructive criticisms. The research leading to these results has received funding from the European Community’s Seventh Framework Programme under grant MUNDUS and HUMOUR-ICT-2008-231724, the European Union’s PASCAL 2 Network of Excellence (ICT-216886), the German Research Foundation DFG project KU 1453-1 , DFG SPP 1527 , MU 987/14-1 , as well as the by the Brain Korea 21 Plus Program of the National Research Foundation of Korea funded by the Ministry of Education, Republic of Korea . This publication only reflects the authors’ views. The authors declare not to have any conflicts of interest.

Funders	Funder number
Seventh Framework Programme	ICT-216886, HUMOUR-ICT-2008-231724
Deutsche Forschungsgemeinschaft	KU 1453-1, MU 987/14-1, DFG SPP 1527
Ministry of Education
National Research Foundation of Korea

Keywords

Brain–computer interfacing
Functional electrical stimulation
Motor imagery
Neuralprosthesis

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10.1016/j.medengphy.2016.06.010

Cite this

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abstract = "Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI–FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.",

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EEG-based BCI for the linear control of an upper-limb neuroprosthesis

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Keywords

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