Journal Article

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

Uddin, Mohammad Shorif, Ahmad, Mohiuddin, Khanam, Farzana, Rahman, Md. Asadur

2020

Overview

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 feature extraction using WPT. Then, for feature selection Shannon entropy, mutual information, and Rényi min-entropy methods are applied. With the selected features, four-class motor imagery EEG signals are classified using several machine learning algorithms. The results suggest that the proposed method is better than the conventional approaches for multiple-class BCI.

• Publisher: Springer Berlin Heidelberg,Springer,Springer Nature B.V,SpringerOpen
• Subject: Algorithms / Artificial Intelligence / Brain–computer interface (BCI) / Cognitive Psychology / Computation by Abstract Devices / Computer Science / Data mining / Electro-encephalogram (EEG) / Electroencephalography / Entropy / Entropy (Information theory) / Feature extraction / Health Informatics / Human-computer interface / Image classification / Machine learning / Machine Learning Techniques for Neuroscience Big Data / Mutual information / Neurosciences / Shannon entropy / Signal classification / Wavelet packet transformation (WPT)