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Brain-computer interface controlled gaming: Evaluation of usability by severely motor restricted end-users
Open accessAbstract
Objective Connect-Four, a new sensorimotor rhythm (SMR) based brain-computer interface (BCI) gaming application, was evaluated by four severely motor restricted end-users; two were in the locked-in state and had unreliable eye-movement.Methods Following the user-centred approach, usability of the BCI prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate (ITR) and subjective workload) and users' satisfaction.Results Online performance varied strongly across users and sessions (median accuracy (%) of end-users: A=.65; B=.60; C=.47; D=.77). Our results thus yielded low to medium effectiveness in three end-users and high effectiveness in one end-user. Consequently, ITR was low (0.05-1.44bits/min). Only two end-users were able to play the game in free-mode. Total workload was moderate but varied strongly across sessions. Main sources of workload were mental and temporal demand. Furthermore, frustration contributed to the subjective workload of two end-users. Nevertheless, most end-users accepted the BCI application well and rated satisfaction medium to high. Sources for dissatisfaction were (1) electrode gel and cap, (2) low effectiveness, (3) time-consuming adjustment and (4) not easy-to-use BCI equipment. All four end-users indicated ease of use as being one of the most important aspect of BCI.Conclusion Effectiveness and efficiency are lower as compared to applications using the event-related potential as input channel. Nevertheless, the SMR-BCI application was satisfactorily accepted by the end-users and two of four could imagine using the BCI application in their daily life. Thus, despite moderate effectiveness and efficiency BCIs might be an option when controlling an application for entertainment.
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“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 [33]
In the citing paper
“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 [33]
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 [33]
Citations
Cites
The CES-D Scale: A Self-Report Depression Scale for Research in the General Population
Radloff, L. S. (1977) · Applied Psychological Measurement · doi:10.1177/014662167700100306 · Cited by 53,267
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080
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Indiveri, G., Linares-Barranco, B., Hamilton, T. J., André, V. S., Etienne‐Cummings, R., Delbrück, T., Liu, S., Dudek, P., Hafliger, P. D., Renaud, S., et al. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00073 · Cited by 1,836
Single-trial analysis and classification of ERP components — A tutorial
Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K.-R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.048 · Cited by 1,138
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Wolpaw, J. R., & Wolpaw, E. W. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388855.001.0001 · Cited by 1,134
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Krusienski, D. J., Sellers, E. W., Cabestaing, F., Bayoudh, S., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/4/007 · Cited by 762
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Botrel, L., Acqualagna, L., Blankertz, B., & Kübler, A. (2017) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2017.08.007 · Cited by 13
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López, S., Cervantes, J.-A., Cervantes, S., Molina, J., & Cervantes, F. (2019) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.09.013 · Cited by 11
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Weyand, S., & Chau, T. T. (2015) · Developmental Neurorehabilitation · doi:10.3109/17518423.2015.1087436 · Cited by 10
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Dwyer, N., Harrison, M., O’Mara, B., & Harley, K. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1379559 · Cited by 6
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Metadata
- Authors
- Holz, E. M., Höhne, J., Staiger-Sälzer, P., Tangermann, M., & Kübler, A.
- Affiliations
- University of Würzburg (DE)
- Technische Universität Berlin (DE)
- University of Freiburg (DE)
- Corresponding author
- Holz, Elisa Mira
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 59
- Issue
- 2
- Pages
- 111-120
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2077189121
- Cited by
- 111
- References
- 51
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 9 · 2023: 12 · 2022: 5 · 2021: 10 · 2020: 9 · 2019: 5 · 2018: 5 · 2017: 6 · 2016: 8 · 2015: 23 · 2014: 12 · 2013: 2
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- European ICT Programme (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, sensorimotor rhythm, usability evaluation, locked-in syndrome, information transfer rate, perceived workload, user satisfaction, Connect Four, event-related potentials, assistive technology
- MeSH terms
- Humans, Middle Aged, Paralysis, Severity of Illness Index, Patient Satisfaction, Brain-Computer Interfaces
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