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Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

Please always quote using this URN: urn:nbn:de:bvb:20-opus-119331
  • 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 shortBrain-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.show moreshow less

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Metadaten
Author: Johannes Höhne, Elisa Holz, Pit Staiger-Sälzer, Klaus-Robert Müller, Andrea Kübler, Michael Tangermann
URN:urn:nbn:de:bvb:20-opus-119331
Document Type:Journal article
Faculties:Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut für Psychologie
Language:English
Parent Title (English):PLoS ONE
Year of Completion:2014
Volume:9
Issue:8
Pagenumber:e104854
Source:PLoS ONE 9(8): e104854. doi:10.1371/journal.pone.0104854
DOI:https://doi.org/10.1371/journal.pone.0104854
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=25162231
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Tag:electroencephalography; eyes; games; hands; machine learning; man-computer interface; signal filtering; social communication
Release Date:2015/10/21
EU-Project number / Contract (GA) number:224631
EU-Project number / Contract (GA) number:216886
EU-Project number / Contract (GA) number:216886
OpenAIRE:OpenAIRE
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung