title: EEG-based BCI for the linear control of an upper-limb neuroprosthesis.
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.
authors.affiliation: Machine Learning Group, Computer Science Faculty, Berlin Institute of Technology, Berlin, Germany. Electronic address: carmen.vidaurre@tu-berlin.de.
authors.forename: Carmen
authors.initials: C
authors.lastname: Vidaurre
authors.affiliation: Control Systems Group, Berlin Institute of Technology, Berlin, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Klauer
authors.affiliation: Control Systems Group, Berlin Institute of Technology, Berlin, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Schauer
authors.affiliation: Institute for Medical Psychology and Behavioral Neurobiology (IMP), University of Tübingen, Tübingen 72076, Germany; TECNALIA, San Sebastian, Spain.
authors.forename: Ander
authors.initials: A
authors.lastname: Ramos-Murguialday
authors.affiliation: Machine Learning Group, Computer Science Faculty, Berlin Institute of Technology, Berlin, Germany; Bernstein Focus: Neurotechnology, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of Korea. Electronic address: klaus-robert.mueller@tu-berlin.de.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D062207:Brain-Computer Interfaces; D002138:Calibration; D004558:Electric Stimulation; D004569:Electroencephalography; D025461:Feedback, Physiological; D006801:Humans; D016014:Linear Models; D058117:Neural Prostheses; D034941:Upper Extremity
keywords: Brain–computer interfacing; Functional electrical stimulation; Motor imagery; Neuralprosthesis