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Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
art. no. 20200013
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
  • SWPS University of Social Sciences and Humanities, Warszawa, Poland
  • Adam Mickiewicz University in Poznań, Poznań, Poland
Bibliografia
  • 1. Wolpaw J, Birbaumer N, Heetderks WJ, Mcfarland D, Hunter Peckham P, Schalk G, et al. Brain-Computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng: A Publication IEEE Eng Med Biol Soc 2000;8:164-73. https://doi. org/10.1109/TRE.2000.847807.confproc.
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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.
  • 5. Regan D. A high frequency mechanism which underlies visual evoked potentials. Electroencephalogr Clin Neurophysiol 1968; 25:231-7. https://doi.org/10.1016/0013-4694(68)90020-5.
  • 6. Oikonomou VP, Liaros G, Georgiadis K, Chatzilari E, Adam K, Nikolopoulos S, et al. Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. arXiv preprint arXiv: 1602.00904; 2016.
  • 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.
  • 9. Nakanishi M, Wang Y, Wang YT, Jung, TP. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. PloS One 2015;10: e0140703. https://doi.org/10.1371/journal.pone.0140703.
  • 10. Bhattacharyya S, Khasnobish A, Chatterjee S, Konar A, Tibarewala DN. Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data. In: 2010 international conference on systems in medicine and biology. IEEE, New Jersey; 2010, 126-31 pp.
  • 11. Kwak NS, Muller KR, Lee SW. A convolutional neural network for steady state visualevoked potential classification under ambulatory environment. PloS One 2017;12:e0172578. https:// doi.org/10.1371/journal.pone.0172578.
  • 12. Lal TN, Schroder M, Hinterberger T, Weston J, Bogdan M, Birbaumer N, Scholkopf B. Support vector channel selection in BCI. IEEE Trans Biomed Eng 2004;51:1003-10. https://doi.org/ 10.1109/TBME.2004.827827.
  • 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.
  • 14. Martinez P, Bakardjian H, Cichocki A. Fully online multi-command brain-computer interface with visual neurofeedback using SSVEP paradigm. London: Hindawi Publishing Corporation; 2007, vol. 2007.
  • 15. Bakardjian H, Tanaka T, Cichocki A. Optimization of SSVEP brain responses with application to eight-command brain–computer interface. Neurosci Lett 2010;469:34-8.
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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
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