Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution

PLoS One. 2014 Aug 27;9(8):e104854. doi: 10.1371/journal.pone.0104854. eCollection 2014.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence*
  • Brain / physiology*
  • Brain-Computer Interfaces*
  • Disabled Persons*
  • Electroencephalography
  • Humans
  • Imagination*
  • Middle Aged
  • Reaction Time
  • Self-Help Devices*
  • Time Factors

Grant support

The authors are grateful for the financial support by several institutions: This work was partly supported by the European Information and Communication Technologies (ICT) Programme Project FP7-224631 and 216886, by the Deutsche Forschungsgemeinschaft (DFG) (grants MU 987/3-2, EXC 1086) and Bundesministerium fur Bildung und Forschung (BMBF) (FKZ 01IB001A, 01GQ0850) and by the FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence, ICT-216886. This work was also supported by the World Class University Program through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, under Grant R31-10008. This publication only reflects the authors' views. Funding agencies are not liable for any use that may be made of the information contained herein. The article processing charge was funded by the open access publication fund of the Albert Ludwigs University Freiburg. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.