The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
Transferring brain-computer interfaces beyond the laboratory: Successful application control for motor-disabled users
Open accessAbstract
Objectives Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI experts around. But are the technology and the field mature enough for this? Especially the successful operation of applications - like text entry systems or assistive mobility devices such as tele-presence robots - requires a good level of BCI control. How much training is needed to achieve such a level? Is it possible to train naïve end-users in 10 days to successfully control such applications?Materials and methods In this work, we report our experiences of training 24 motor-disabled participants at rehabilitation clinics or at the end-users' homes, without BCI experts present. We also share the lessons that we have learned through transferring BCI technologies from the lab to the user's home or clinics.Results The most important outcome is that 50% of the participants achieved good BCI performance and could successfully control the applications (tele-presence robot and text-entry system). In the case of the tele-presence robot the participants achieved an average performance ratio of 0.87 (max. 0.97) and for the text entry application a mean of 0.93 (max. 1.0). The lessons learned and the gathered user feedback range from pure BCI problems (technical and handling), to common communication issues among the different people involved, and issues encountered while controlling the applications.Conclusion The points raised in this paper are very widely applicable and we anticipate that they might be faced similarly by other groups, if they move on to bringing the BCI technology to the end-user, to home environments and towards application prototype control.
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In the citing paper
“Firstly, works where users acquired BCI control (able-bodied [3,25] and end-users [40,41,51,62,63]) do not report any learning metric over time.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [51]
In the citing paper
“Secondly, other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy [9,27,30,31,33,35,51] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [51]
In the citing paper
“The Brain Tweakers participation in the Cybathlon BCI race relied on the EEG-based MI BCI design previously developed in CNBI, which had already been shown to allow end-users to successfully operate a number of BCI prototypes [51].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [51]
In the citing paper
“We denote that our training apparatus, which certainly falls under the category of “conventional” MI BCI training protocols relying on visual feedback training on top of initial machine calibration with spontaneous SMRs, has been very similar to the one we have applied in our previous work [51].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [51]
In the citing paper
“Second, training with the BCI application rather than towards it—like in [51] and most other studies—had a profound impact, as shown in Fig 6, for both application performances and subject learning.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [51]
In the citing paper
“There, a considerable number of end-users failed to acquire BCI control, especially those without distinct spontaneous SMRs at training onset.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [51]
In the citing paper
“P1 had several years prior participated in the MI BCI studies reported in [35,51,71], while P2 was BCI naive at the onset of his Cybathlon training.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [51]
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Metadata
- Authors
- Leeb, R., Perdikis, S., Tonin, L., Biasiucci, A., Tavella, M., Creatura, M., Molina, A., Al-Khodairy, A., Carlson, T., & Millán, J. D. R.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Clinique Romande de Réadaptation (CH)
- Corresponding author
- Leeb, Robert
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 59
- Issue
- 2
- Pages
- 121-132
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W1999296691
- Cited by
- 166
- References
- 49
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 7 · 2022: 14 · 2021: 9 · 2020: 20 · 2019: 11 · 2018: 11 · 2017: 12 · 2016: 17 · 2015: 21 · 2014: 14 · 2013: 4
- FWCI
- 10.20
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- European ICT programme project TOBI: Tools for Brain-Computer Interaction
- 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 impairment, assistive technology, end-user training, technology transfer, rehabilitation centers, user feedback, BCI performance
- MeSH terms
- Adult, Electroencephalography, Female, Persons with Disabilities, Humans, Male, Middle Aged, Paralysis, Brain-Computer Interfaces
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