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

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

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....

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Bibliographic Details
Main Authors: Md. Asadur Rahman, Farzana Khanam, Mohiuddin Ahmad, Mohammad Shorif Uddin
Format: Article
Language:English
Published: SpringerOpen 2020-06-01
Series:Brain Informatics
Subjects:
Electro-encephalogram (EEG)
Brain–computer interface (BCI)
Feature extraction
Wavelet packet transformation (WPT)
Shannon entropy
Mutual information
Online Access:http://link.springer.com/article/10.1186/s40708-020-00108-y

Internet

http://link.springer.com/article/10.1186/s40708-020-00108-y

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