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• W3201848666 abstract "The aim of this study was to find an efficient method to determine features that characterize octave illusion data. Specifically, this study compared the efficiency of several automatic feature selection methods for automatic feature extraction of the auditory steady-state responses (ASSR) data in brain activities to distinguish auditory octave illusion and nonillusion groups by the difference in ASSR amplitudes using machine learning. We compared univariate selection, recursive feature elimination, principal component analysis, and feature importance by testifying the results of feature selection methods by using several machine learning algorithms: linear regression, random forest, and support vector machine. The univariate selection with the SVM as the classification method showed the highest accuracy result, 75%, compared to 66.6% without using feature selection. The received results will be used for future work on the explanation of the mechanism behind the octave illusion phenomenon and creating an algorithm for automatic octave illusion classification." @default.
• W3201848666 created "2021-10-11" @default.
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• W3201848666 date "2021-09-25" @default.
• W3201848666 modified "2023-10-14" @default.
• W3201848666 title "Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning" @default.
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• W3201848666 doi "https://doi.org/10.3390/s21196407" @default.
• W3201848666 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8512176" @default.
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