Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution
Open accessAbstract
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.
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Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of users to voluntarily modulate their brain signals (subject learning) is scarce, most often indirect and rather inconclusive.
“Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of the users to voluntary modulate their brain signals (subject learning) is, in fact, scarce, most often indirect and rather inconclusive.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [34]
Mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy or application performances.
“Indeed, mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy [25,27,30–35] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [34]
Some works find no evident learning effects at the neural correlate level.
“Some works find no evident learning effects at the neural correlate level [25,34].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [34]
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- Authors
- Höhne, J., Holz, E., Staiger-Sälzer, P., Müller, K.-R., Kübler, A., & Tangermann, M.
- Affiliations
- Technische Universität Berlin (DE)
- University of Würzburg (DE)
- Korea University (KR)
- University of Freiburg (DE)
- Corresponding author
- Höhne, Johannes
- Müller, Klaus-Robert
- Kübler, Andrea
- Tangermann, Michael
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-08-27
- Volume
- 9
- Issue
- 8
- Pages
- e104854
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4146550
- OpenAlex
- W2163486304
- Cited by
- 85
- References
- 63
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 6 · 2020: 8 · 2019: 12 · 2018: 9 · 2017: 6 · 2016: 9 · 2015: 15 · 2014: 1
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- Online Publication Service of Würzburg University (Würzburg University) (submittedVersion, cc-by)
- Funders
- European Commission (216886)
- National Research Foundation
- Deutsche Forschungsgemeinschaft (01GQ0850, MU 987/3-2, EXC‐1086)
- Bundesministerium für Bildung und Forschung (01GQ0850, 01IB001A)
- National Research Foundation of Korea (R31‐10008, 01GQ0850)
- FP7 Information and Communication Technologies (FP7-224631)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- brain-computer interface, motor imagery, locked-in state, EEG-based control, assistive technology, user-centered design, information transfer rate, communication difficulties
- MeSH terms
- Artificial Intelligence, Brain, Electroencephalography, Persons with Disabilities, Disabled Persons, Humans, Imagination, Middle Aged, Reaction Time, Self-Help Devices, Time Factors, Brain-Computer Interfaces
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