Towards Independence: A BCI Telepresence Robot for People With Severe Motor Disabilities
Open accessAbstract
This paper presents an important step forward towards increasing the independence of people with severe motor disabilities, by using brain-computer interfaces to harness the power of the Internet of Things. We analyze the stability of brain signals as end-users with motor disabilities progress from performing simple standard on-screen training tasks to interacting with real devices in the real world. Furthermore, we demonstrate how the concept of shared control-which interprets the user's commands in context-empowers users to perform rather complex tasks without a high workload. We present the results of nine end-users with motor disabilities who were able to complete navigation tasks with a telepresence robot successfully in a remote environment (in some cases in a different country) that they had never previously visited. Moreover, these end-users achieved similar levels of performance to a control group of 10 healthy users who were already familiar with the environment.
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It may be hard to obtain sufficiently large patient populations for concrete and statistically significant conclusions.
“Additionally, it is apparent from previous work that access to sufficiently large patient populations for concrete and statistically significant conclusions may be difficult to obtain (1–5, 8, 11, 29, 39).”
From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [8]
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 [35]
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 [35]
Other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy or application performances.
“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 [35]
P1 had several years prior participated in MI BCI studies, while P2 was BCI naive at the onset of his Cybathlon training.
“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 [35]
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References 44 · Cited by 187
- Authors
- Leeb, R., Tonin, L., Rohm, M., Desideri, L., Carlson, T., & Millan, J. D. R.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- University of Padua (IT)
- Heidelberg University (DE)
- AIAS Bologna (IT)
- University College London (GB)
- Venue
- Proceedings of the IEEE
- Published
- 2015-06
- Volume
- 103
- Issue
- 6
- Pages
- 969-982
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W1638736765
- Cited by
- 187
- References
- 44
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 15 · 2023: 16 · 2022: 19 · 2021: 15 · 2020: 28 · 2019: 27 · 2018: 24 · 2017: 15 · 2016: 12 · 2015: 5
- FWCI
- 11.22
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- Proceedings of the IEEE (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 (FP7-224631)
- Swiss National Science Foundation
- 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, telepresence robots, severe motor impairment, shared control, Internet of Things, remote navigation, user independence, assistive technology
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