ArticleBrain informatics2020
Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation.
Article in Brain informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Influence of EEG Signal Augmentation Methods on Classification Accuracy of Motor Imagery Events.Sensors (Basel, Switzerland) · 2026Article
- Entropy and Complexity in QEEG Reveal Visual Processing Signatures in Autism: A Neurofeedback-Oriented and Clinical Differentiation Study.Brain sciences · 2025Article
- Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach.Frontiers in psychiatry · 2025Article
- Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation.Brain informatics · 2024Article
- Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals.Brain informatics · 2023Article
- Emotional State Classification from MUSIC-Based Features of Multichannel EEG Signals.Bioengineering (Basel, Switzerland) · 2023Article
- SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring.Frontiers in physiology · 2023Article
- Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy.Entropy (Basel, Switzerland) · 2021Article
- SPECTRA: a tool for enhanced brain wave signal recognition.BMC bioinformatics · 2021Article
- The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.PeerJ. Computer science · 2021Article
- The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface.Journal of healthcare engineering · 2021Article
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4 authors.
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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.
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