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Objectives: Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy. Methods: System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI). Results: The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects. Conclusions: It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.
Czasopismo
Rocznik
Tom
Strony
art. no. 20200013
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
- SWPS University of Social Sciences and Humanities, Warszawa, Poland
autor
- Adam Mickiewicz University in Poznań, Poznań, Poland
Bibliografia
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- 4. Guger C, Schlogl A, Neuper C, Walterspacher D, Strein T, Pfurtscheller G. Rapid prototyping of an EEG-based braincomputer interface (BCI). IEEE Trans Neural Syst Rehabil Eng 2001;9:49-58. https://doi.org/10.1109/7333.918276.
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- 7. Hakvoort G, Reuderink B, Obbink M. Comparison of PSDA and CCA detection methods in a SSVEP-based BCI-system. Netherlands: Centre for Telematics & Information Technology University of Twente; 2011.
- 8. Lin Z, Zhang C, Wu W, Gao X. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 2006;53:2610-4. https://doi.org/10.1109/TBME. 2006.886577.
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- 13. Bakardjian H. Optimization of steady-state visual responses for robust brain-computer interfaces. Tokyo: Department of Electronic and Information Engineering Tokyo University of Agriculture and Technology; 2010.
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- 31. Chen X, Wang Y, Nakanishi M, Gao X, Jung TP, Gao S. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci Unit States Am 2015;112:E6058-67. https://doi.org/10. 1073/pnas.1508080112.
- 32. Fatourechi M, Bashashati A, Ward RK, Birch GE. A hybrid genetic algorithm approach for improving the performance of the LF-ASD brain computer interface. In: Proceedings. (ICASSP ’05). IEEE international conference on acoustics, speech, and signal processing, 2005. IEEE, New Jersey; 2005, 345-8 pp, vol. 5. https://doi.org/10.1109/ICASSP.2005.1416311.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-454de36a-775d-4d6e-83bc-3d46ce247517