Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller
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OBJECTIVE: While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. APPROACH: This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. MAIN RESULTS: We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. SIGNIFICANCE: This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.
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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 [71]
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Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance
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Detection of motor imagery based on short-term entropy of time–frequency representations
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Context-Aware Learning for Generative Models
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Efficient human-machine control with asymmetric marginal reliability input devices
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Self-citationEvaluation of induced and evoked changes in EEG during selective attention to verbal stimuli
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Adaptive Brain-computer Interfacing through Error-related Potential Detection
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BMI: Lessons from tests with impaired users
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Motor Imagery Brain–Computer Interface for RPAS Command and Control
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Future Directions for Brain-Machine Interfacing Technology
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Moving Brain-Controlled Devices Outside the Lab: Principles and Applications
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Stretchable, hair-compatible, and long-term stable wearable EEG systemPreprint
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Willpower and Epistemic Agency
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Comparison of Image-Based Deep Learning Classification Models for Motor Imagery EEG Signals
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Self-citation
- Authors
- Perdikis, S., Leeb, R., Williamson, J., Ramsay, A., Tavella, M., Desideri, L., Hoogerwerf, E.-J., Al-Khodairy, A., Murray-Smith, R., & Millán, J. D. R.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- University of Glasgow (GB)
- AIAS Bologna (IT)
- Clinique Romande de Réadaptation (CH)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2014-04-16
- Volume
- 11
- Issue
- 3
- Pages
- 036003
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W1989664338
- Cited by
- 85
- References
- 43
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 7 · 2023: 4 · 2022: 15 · 2021: 5 · 2020: 10 · 2019: 3 · 2018: 6 · 2017: 4 · 2016: 12 · 2015: 11 · 2014: 4
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Open Access at Essex (University of Essex) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- brain-computer interface, motor imagery, hybrid BCI, Braintree, severely disabled users, usability, model-based analysis, error handling, augmentative and alternative communication
- MeSH terms
- Adult, Algorithms, Brain Mapping, Communication Devices for People with Disabilities, Communication Aids for Disabled, Electroencephalography, Female, Humans, Imagination, Language, Male, Motor Cortex, Movement, Pattern Recognition, Automated, Sensitivity and Specificity, Software, User-Computer Interface, Reproducibility of Results, Evoked Potentials, Motor, Brain-Computer Interfaces
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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