Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation

This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain-computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the case of multiple-class problem, classification accuracy solely depends on the effective feature selection from the WPT features. In conventional approaches, Shannon entropy and mutual information methods are often used to select the features. In this work, we have shown that our proposed Rényi min-entropy-based approach outperforms the conventional methods for multiple EEG signal classification. The dataset of BCI competition-IV (contains 4-class motor imagery EEG signal) is used for this experiment. The data are preprocessed and separated as the classes and used for the…