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    Evaluation of wavelet features selected via statistical evidence from steady-state visually-evoked potentials to predict the stimulating frequency
    (2021) Sayilgan, Ebru; Yüce, Yılmaz Kemal; İşler, Yalçın
    Electroencephalography (EEG) is a noninvasive method to record brain activities. Among different EEG recording methods, the recording, while a visual stimulation is shown to the subject, is one of the most popular methods. Recently, steady-state visually-evoked potentials (SSVEP) where visual objects are blinking at a fixed frequency have been commonly-used method in brain-computer interfaces. Although various features extracted from SSVEP records have been used, the use of features from wavelet transform should be preferred due to the nonstationary structure of these signals. In this study, the combination of mother wavelet and classifier, which gives the highest accuracy to determine the stimulating frequency, is examined by applying common wavelet features to inputs of classifiers. Features of energy, variance, and entropy were extracted for well-known five EEG frequency bands using six different mother wavelets. Then, classifier performances of six basic classifiers were compared. This study was run for both each subjects individually and all subjects together. Results showed that (i) ANOVA-based feature selection reduces the performances, (ii) there is no unique combination of classifier and mother wavelet while evaluating each subject individually, (iii) the highest performance was achieved by combination of ensemble learner and Reverse Biorthogonal wavelet while evaluating all subjects together.
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    Öğe
    Principal component analysis and manifold learning techniques for the design of brain-computer interfaces based on steady-state visually evoked potentials
    (Elsevier, 2023) Yesilkaya, Bartu; Sayilgan, Ebru; Yuce, Yilmaz Kemal; Perc, Matjaz; Isler, Yalcin
    Steady-state visually evoked potentials (SSVEP) are stochastic and nonstationary bioelectric signals. Because of these properties, it is difficult to achieve high classification accuracy, especially when many considered features lead to a complex structure. We therefore propose a manifold learning framework to decrease the number of features and to classify SSVEP data by comparing lower dimensional matrices with well-known machine learning algorithms. We use the AVI-SSVEP Dataset, which includes stimuli at seven different frequencies and 15360 samples per person. The SSVEP features are extracted from relevant and distinctive frequency -domain, time-domain, and time-frequency domain properties, creating a total of 55 feature vectors. We then analyze and compare five divergent manifold learning methods with respect to their performance on nine different machine-learning algorithms. Among all considered manifold learning methods, we show that the Principal Component Analysis has the best classifier performance with an average of 22 components. Moreover, the Naive Bayes classifier with the Principal Component Analysis achieves the maximum accuracy of 50.0%-80.95% for a 7-class classification problem. Our research thus shows that the proposed analytical framework can significantly improve the decoding accuracy of 7-class SSVEP problems, and that it exhibits notable robustness and efficiency for small group datasets.