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.
Highlights
Brain–computer interface (BCI) is a modern notion that enables the way of generating communication between computer and brain functionalities
The brain functionality can be assessed in two different ways: electrical activities (suggested modalities are electro-corticogram (ECoG), electro-encephalogram (EEG), and magneto-encephalogram (MEG)) and hemodynamics
3 Results and discussion As the approach of the proposal, 4-class motor imagery EEG signals are collected from the brain–computer interface (BCI) competitionIV
Summary
Brain–computer interface (BCI) is a modern notion that enables the way of generating communication between computer and brain functionalities. The brain functionality can be assessed in two different ways: electrical activities (suggested modalities are electro-corticogram (ECoG), electro-encephalogram (EEG), and magneto-encephalogram (MEG)) and hemodynamics (functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), For providing the MI movement-based stimuli a candidate imagines the pattern of the real executive movements. During such MI movement, the EEG signals are recorded from the scalp of the participant that can be used to control the switches through a computer-based signal processing and this is broadly called BCI.
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