The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities
Abstract
Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals. Considering the many differences in body functions and usage scenarios between individuals with disabilities and able-bodied individuals, involvement of the target population in BCI evaluation is necessary. In this review, 39 studies reporting EEG-oriented BCI assessment by individuals with disabilities were identified in the past decade. With respect to participant populations, a need for assessing BCI performance for the pediatric population with severe disabilities was identified as an important future direction. Acquiring a reliable communication pathway during early stages of development is crucial in avoiding learned helplessness in pediatric-onset disabilities. With respect to evaluation, augmenting traditional measures of system performance with those relating to contextual factors was recommended for realizing user-centered designs appropriate for integration in real-life. Considering indicators of user state and developing more effective training paradigms are recommended for future studies of BCI involving individuals with disabilities.
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Cites
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Cited by
Deep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751
Visual and Auditory Brain–Computer Interfaces
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Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces
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Towards Independence: A BCI Telepresence Robot for People With Severe Motor Disabilities
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Kwon, J., Shin, J., & Im, C.-H. (2020) · PLOS ONE · doi:10.1371/journal.pone.0230491 · Cited by 53
Metadata
- Authors
- Moghimi, S., Kushki, A., Marie Guerguerian, A., & Chau, T.
- Venue
- Assistive Technology
- Publisher
- Informa UK Limited
- Published
- 2013-04-03
- Volume
- 25
- Issue
- 2
- Pages
- 99-110
- ISSN
- 1040-0435 (ISSN-L)
- 1949-3614
- OpenAlex
- W2021098468
- Cited by
- 166
- References
- 19
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- closed
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