Semantic Category-Based Classification Using Nonlinear Features and Wavelet Coefficients of Brain Signals

Abstract

The problem of object recognition is solved in the brain using different strategies. These strategies are to some extent known to neuroscientists, but researches on this issue are still in progress to understand more accurately the computational, anatomical, and physiological aspects of this fast and accurate capability of the brain. In this paper, we presented a method, based on extracting nonlinearity of signals such as L-Z complexity, fractal dimension, Lyapunov exponents, Hurst exponents, and entropy, to classify single trials into their related semantic category groups with a linear SVM classifier. Furthermore, we proposed to combine nonlinear features mentioned above with wavelet coefficients to improve the classification accuracy. EEG signals were recorded from human subjects according to 10–20 system while performing a “go/no go” object-categorization task. Combining nonlinear features with wavelet coefficients led to a significant enhancement in classification accuracy (73%) relative to wavelet coefficients alone (54%). Feature-selection results showed that a significantly larger proportion of final selected features include nonlinear features (44%) relative to the first ratio of them (14%) to whole features. This ratio enhancement demonstrates the essential role of nonlinear features in the obtained classification accuracy. In addition, C3 channel and Katz fractal dimension were introduced as the most informative channel and the best nonlinear feature, respectively.