Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning

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.

Multi-domain feature fusion for target color decoding from multichannel neural recordings of pigeons

“…For the optimal feature subsets selected by different feature selection methods, we use linear discriminant analysis (LDA) [22], support vector machine (SVM) [23], and convolutional neural network (CNN) [24] to verify the decoding performance. The structure of the CNN is shown in Figure 2.…”