Principal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentials

McFarland, 2011, Brain-computer interfaces for communication and control, Commun. ACM, 54, 60, 10.1145/1941487.1941506 Schalk, 2004, BCI2000: a general-purpose brain-computer interface (BCI) system, IEEE Trans. Biomed. Eng., 51, 1034, 10.1109/TBME.2004.827072 Vidal, 1977, Real-time detection of brain events in EEG, Proc. IEEE, 65, 633, 10.1109/PROC.1977.10542 Chen, 2015, High-speed spelling with a noninvasive brain-computer interface, Proc. Natl. Acad. Sci. USA, 112, E6058, 10.1073/pnas.1508080112 Wang, 2006, A practical VEP-based brain-computer interface, IEEE Trans. Neural Syst. Rehabil. Eng., 14, 234, 10.1109/TNSRE.2006.875576 Regan, 1989 Wang, 2008, Brain-computer interfaces based on visual evoked potentials, IEEE Eng. Med. Biol., 27, 64, 10.1109/MEMB.2008.923958 Gao, 2014, Visual and auditory brain-computer interfaces, IEEE Trans. Biomed. Eng., 61, 1436, 10.1109/TBME.2014.2300164 Regan, 1982, Comparison of transient and steady-state methods, Ann. NY Acad. Sci., 388, 45, 10.1111/j.1749-6632.1982.tb50784.x E. Basar, EEG-Brain Dynamics: Relation Between EEG and Brain Evoked Potentials, in: Brain Lang 1980, Elsevier, Amsterdam. Sayilgan, 2021, Evaluation of wavelet features selected via statistical evidence from steady-state visually-evoked potentials to predict the stimulating frequency, J. Fac. Eng. Archit. Gazi Univ., 36, 593 Sayilgan, 2021, Evaluation of mother wavelets on steady-state visually-evoked potentials for triple-command brain-computer interfaces, Turk. J. Electr. Eng. Comput. Sci., 29, 2263, 10.3906/elk-2010-26 Duda, 2000 Roweis, 2000, Nonlinear dimensionality reduction by locally linear embedding, Science, 290, 2323, 10.1126/science.290.5500.2323 Tenenbaum, 2000, A global geometric framework for nonlinear dimensionality reduction, Science, 290, 2319, 10.1126/science.290.5500.2319 Coifman, 2005, Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, Appl. Math., 102, 7426 Van der Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579 Hinton, 2003, Stochastic neighbor embedding, 857 Li, 2022, Analysis and visualization of accidents severity based on LightGBM-TPE, Chaos Solitons Fractals, 157, 10.1016/j.chaos.2022.111987 Yesilkaya, 2022, Manifold learning methods for the diagnosis of ovarian cancer, J. Comput. Sci., 63, 10.1016/j.jocs.2022.101775 Tyagi, 2017, A comparison of feature extraction and dimensionality reduction techniques for EEG-based BCI system, IUP J. Comput. Sci., 11, 51 You, 2020, Motor imagery EEG classification based on flexible analytic wavelet transform, Biomed. Signal Process. Control, 62, 10.1016/j.bspc.2020.102069 Li, 2016, Extracting the nonlinear features of motor imagery EEG using parametric t-SNE, Neurocomputing, 218, 371, 10.1016/j.neucom.2016.08.083 E. Krivov, M. Belyaev, Dimensionality Reduction with Isomap Algorithm for EEG Covariance Matrices, in: 2016 4th International Winter Conference on Brain-Computer Interface, BCI, IEEE, pp. 1–4, http://dx.doi.org/10.1109/IWW-BCI.2016.7457448. Li, 2019, Feature extraction and visualization of MI-EEG with L-MVU algorithm, 835 Sadiq, 2021, Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces, Expert Syst. Appl., 164, 10.1016/j.eswa.2020.114031 Li, 2017, Adaptive feature extraction of motor imagery EEG with optimal wavelet packets and SE-Isomap, Appl. Sci., 7, 390, 10.3390/app7040390 Tan, 2020, Dimensionality reduction in evolutionary algorithms-based feature selection for motor imagery brain-computer interface, Swarm Evol. Comput., 52, 10.1016/j.swevo.2019.100597 Bischof, 2004, Feature mapping using pca. Locally linear embedding and isometric feature mapping for EEG-based brain computer interface, 189 Gan, 2006 Vilic, 2013 Carvalho, 2015, Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs, Biomed. Signal Process. Control, 21, 34, 10.1016/j.bspc.2015.05.008 Zerafa, 2018, To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs, J. Neural Eng., 15, 1001, 10.1088/1741-2552/aaca6e Sayilgan, 2022, Investigating the effect of flickering frequency pair and mother wavelet selection in steady-state visually-evoked potentials on two-command brain-computer interfaces, IRBM, 43, 594, 10.1016/j.irbm.2022.04.006 Sayilgan, 2020, Duragan Durum Gorsel Uyaran Potansiyellerinden Fourier Donusumu ile Uc Farklı Frekansın Kestirimi, Duzce Univ. Bilim Teknoloji Derg., 8, 2337 Sayilgan, 2021, Frequency recognition from temporal and frequency depth of the brain-computer interface based on steady-state visual evoked potentials, J. Intell. Syst. Appl., 4, 68, 10.54856/jiswa.202105160 Degirmenci, 2021, Evaluation of Wigner–Ville distribution features to estimate steady-state visual evoked potentials’ stimulation frequency, J. Intell. Syst. Appl., 4, 133, 10.54856/jiswa.202112178 Avci, 2022, Design of steady-state visually-evoked potential based brain-computer interface system, J. Intell. Syst. Appl., 5, 86, 10.54856/jiswa.202212214 Sayilgan, 2021, Evaluating steady-state visually evoked potentials-based brain-computer interface system using wavelet features and various machine learning methods Wold, 1987, Principal component analysis, Chemometr. Intell. Lab. Syst., 2, 37, 10.1016/0169-7439(87)80084-9 Selek, 2021, The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis, Proc. Inst. Mech. Eng. H, 235, 1479, 10.1177/09544119211036806 Torgerson, 1952, Multidimensional scaling: I. Theory and method, Psychometrika, 17, 401, 10.1007/BF02288916 Hout, 2013, Multidimensional scaling, WIREs Cogn. Sci., 4, 93, 10.1002/wcs.1203 Turhan, 2004 Lopes, 2022, Machine learning partners in criminal networks, Sci. Rep., 12, 15746, 10.1038/s41598-022-20025-w Bottou, 1991, Stochastic gradient learning in neural networks, Proc. Neuro-Nimes, 91, 12 Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. System Sci., 55, 119, 10.1006/jcss.1997.1504 Barstugan, 2020, The effect of dictionary learning on weight update of AdaBoost and ECG classification, J. King Saud Univ. Comput. Inf. Sci., 32, 1149 Biau, 2016, A random forest guided tour, TEST, 25, 197, 10.1007/s11749-016-0481-7 Bishop, 2006, Pattern recognition, Mach. Learn., 128, 2006 Zhang, 2004 Mukherjee, 2012, Intrusion detection using naive Bayes classifier with feature reduction, Proc. Technol., 4, 119, 10.1016/j.protcy.2012.05.017 Berkson, 1944, Application of the logistic function to bio-assay, J. Amer. Statist. Assoc., 39, 357 Gok, 2011, Forecasting the sector portions of banks by logistic regression analysis, Dokuz Eylul Univ. Isletme Fak. Derg., 12, 43 Vapnik, 1995 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324