TY  - JOUR
A1  - Höhne, Johannes
A1  - Holz, Elisa
A1  - Staiger-Sälzer, Pit
A1  - Müller, Klaus-Robert
A1  - Kübler, Andrea
A1  - Tangermann, Michael
T1  - Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution
T2  - PLoS ONE
N2  - 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.
KW  - eyes
KW  - signal filtering
KW  - social communication
KW  - hands
KW  - machine learning
KW  - man-computer interface
KW  - games
KW  - electroencephalography
Y1  - 2014
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/11933
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-119331
VL  - 9
IS  - 8
ER  -