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

Brain Informatics

Farzana Khanam¹  Md. Asadur Rahman²  Mohammad Shorif Uddin³  Mohiuddin Ahmad⁴ 

[1] Department of Biomedical Engineering, Jashore University of Science and Technology (JUST);Department of Biomedical Engineering, Military Institute of Science & Technology (MIST);Department of Computer Science and Engineering, Jahangirnagar University;Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET);

关键词: Electro-encephalogram (EEG);    Brain–computer interface (BCI);    Feature extraction;    Wavelet packet transformation (WPT);    Shannon entropy;    Mutual information;   

DOI  :  10.1186/s40708-020-00108-y

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown