The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Ica: a potential tool for bci systems
Open accessAbstract
Several studies dealing with independent component analysis (ICA)-based brain-computer interface (BCI) systems have been reported. Most of them have only explored a limited number of ICA methods, mainly FastICA and INFOMAX. The aim of this article is to help the BCI community researchers, especially those who are not familiar with ICA techniques, to choose an appropriate ICA method. For this purpose, the concept of ICA is reviewed and different measures of statistical independence are reported. Then, the application of these measures is illustrated through a brief description of the widely used algorithms in the ICA community, namely SOBI, COM2, JADE, ICAR, FastICA, and INFOMAX. The implementation of these techniques in the BCI field is also explained. Finally, a comparative study of these algorithms, conducted on simulated electroencephalography (EEG) data, shows that an appropriate selection of an ICA algorithm may significantly improve the capabilities of BCI systems.
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Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
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
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Motor imagery classification by means of source analysis for brain–computer interface applications
Qin, L., Ding, L., & He, B. (2004) · Journal of Neural Engineering · doi:10.1088/1741-2560/1/3/002 · Cited by 239
Cited by
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Visual and Auditory Brain–Computer Interfaces
Gao, S., Wang, Y., Gao, X., & Hong, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300164 · Cited by 449
EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey
Hwang, H.-J., Kim, S., Choi, S., & Im, C.-H. (2013) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2013.780869 · Cited by 257
Lin, C.-T., Ko, L.-W., Chang, M.-H., Duann, J.-R., Chen, J.-Y., Su, T.-P., & Jung, T.-P. (2010) · Gerontology · doi:10.1159/000230807 · Cited by 133
Metadata
- Authors
- Kachenoura, A., Albera, L., Senhadji, L., & Comon, P.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2008
- Volume
- 25
- Issue
- 1
- Pages
- 57-68
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W1971274817
- Cited by
- 225
- References
- 4
- FWCI
- 8.12
- Type
- journal article
- Language
- en
- Open access
- green
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- Kachenoura, A., Albera, L., Senhadji, L., & Comon, P. (2008). Ica: a potential tool for bci systems. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2008.4408442
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