Motor Imagery EEG Signals Classification Based on Mode Amplitude and Frequency Components Using Empirical Wavelet Transform

The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.

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Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features

International Journal Bioautomation ◽

10.7546/ijba.2021.25.1.000611 ◽

2021 ◽

Vol 25 (1) ◽

pp. 13-24

Author(s):

Zabir Al Nazi ◽

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A. B. M. Aowlad Hossain ◽

Md. Monirul Islam ◽

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...

Keyword(s):

Wavelet Transform ◽

Motor Imagery ◽

Physical Disabilities ◽

Variable Length ◽

Discrete Wavelet ◽

Eeg Signal ◽

Random Subspace ◽

Complex Wavelet Transform ◽

Eeg Signals ◽

Random Subspace Ensemble

Classification of electroencephalography (EEG) signals for brain-computer interface has great impact on people having various kinds of physical disabilities. Motor imagery EEG signals of hand and leg movement classification can help people whose limbs are replaced by prosthetics. In this paper, random subspace ensemble network with variable length feature sampling has been proposed for improving the prediction accuracy of motor imagery EEG signal classification. The method has been tested on eight different subjects and a hybrid dataset of two subjects data combined. Discrete wavelet transform based de-noising scheme has been adopted to remove artifacts from the EEG signal. For sub-band selection, dual-tree complex wavelet Transform has been employed. Mutual information scoring has been used for univariate feature selection from the feature space. A comparative analysis has been carried out where random subspace ensemble network outperformed other classification models. The maximum accuracy obtained by the model was 90.00%. Furthermore, the model showed better performance on the hybrid dataset with an average accuracy of 86.00%. The findings of this study are expected to be useful in artificial limb movements through brain-computer interfacing for rehabilitation of people with such physical disabilities.

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Empirical Wavelet Transform; Stationary and Nonstationary Signals

Journal of Electronic & Information Systems ◽

10.30564/jeisr.v1i2.1008 ◽

2020 ◽

Vol 1 (2) ◽

Author(s):

Sedigheh Ghofrani

Keyword(s):

Wavelet Transform ◽

Digital Signal ◽

Signal Decomposition ◽

Early Nineteenth Century ◽

Nonstationary Signals ◽

Time Frequency ◽

Frequency Representation ◽

Empirical Wavelet Transform ◽

Frequency Components ◽

The Relationship

Signal decomposition into the frequency components is one of the oldest challenges in the digital signal processing. In early nineteenth century, Fourier transform (FT) showed that any applicable signal can be decomposed by unlimited sinusoids. However, the relationship between time and frequency is lost under using FT. According to many researches for appropriate time-frequency representation, in early twentieth century, wavelet transform (WT) was proposed Wavelet transform (WT) is a well-known method which developed in order to decompose a signal into frequency components. In contrast with original WT which is not adaptive according to the input signal, empirical wavelet transform (EWT) was proposed to overcome this problem. In this paper, the performance of WT and EWT in terms of signal decomposing into basic components are compared. For this purpose, a stationary signal include five sinusoids and ECG as biomedical and nonstationary signal are used. Due to being non-adaptive, WT may remove signal components but EWT because of being adaptive is appropriate. EWT can also extract the baseline of ECG signal easier than WT.

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