title: Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution.
abstract: Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.
authors.affiliation: Neurotechnology group, Berlin Institute of Technology, Berlin, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Höhne
authors.affiliation: Department of Psychology I, University of Würzburg, Würzburg, Germany.
authors.forename: Elisa
authors.initials: E
authors.lastname: Holz
authors.affiliation: Beratungsstelle für Unterstützte Kommunikation (BUK), Diakonie Bad Kreuznach, Bad Kreuznach, Germany.
authors.forename: Pit
authors.initials: P
authors.lastname: Staiger-Sälzer
authors.affiliation: Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul, Korea.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.affiliation: Department of Psychology I, University of Würzburg, Würzburg, Germany.
authors.forename: Andrea
authors.initials: A
authors.lastname: Kübler
authors.affiliation: BrainLinks-BrainTools Excellence Cluster, University of Freiburg, Freiburg, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Tangermann
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D006233:Persons with Disabilities; D004569:Electroencephalography; D006801:Humans; D007092:Imagination; D008875:Middle Aged; D011930:Reaction Time; D012656:Self-Help Devices; D013997:Time Factors