The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges
Open accessAbstract
In recent years, new research has brought the field of electroencephalogram (EEG)-based brain-computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, "Communication and Control", "Motor Substitution", "Entertainment", and "Motor Recovery". We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users' mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.
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Cites
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Cited by
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115
Review of the BCI Competition IV
Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., et al. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00055 · Cited by 1,288
Control strategies for active lower extremity prosthetics and orthotics: a review
Tucker, M. R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., Millán, J. D. R., Riener, R., Vallery, H., & Gassert, R. (2015) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-12-1 · Cited by 1,047
“Even so, a recent pilot study investigated the use of an fNIRS-BCI to detect the preparation for movement of the hip in seated stroke subjects, which may indicate its suitability in shared control with severely impaired subjects [50].”
InfluentialSelf-citationBrain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke
Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., Schnider, A., Schmidlin, T., Zhang, H., Bassolino, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04673-z · Cited by 649
Self-citationAn overview of the Internet of Things for people with disabilities
Domingo, M. C. (2012) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2011.10.015 · Cited by 638
Metadata
- Authors
- Millán, J. D. R.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2010
- Volume
- 1
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2154037495
- Cited by
- 866
- References
- 13
- FWCI
- 32.77
- Type
- journal article
- Language
- en
- Open access
- gold
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