Multivariate empirical mode decomposition and multiscale entropy analysis of EEG signals from SSVEP-based BCI system

The high accuracy of electroencephalogram (EEG) signal classification is the premise for the wide application of brain computer interface (BCI). In this paper, a hybrid method consisting of multivariate empirical mode decomposition (MEMD) and common space pattern (CSP) is proposed to recognize left-hand and right-hand hypothetical motion from EEG signals. Experiments were carried out using the BCI competition II imagery database. EEG signals were decomposed into multiple intrinsic mode functions (IMFs) by MEMD. The IMF functions with high correlation were processed by CSP, and AR coefficients and entropy values were extracted as features. After genetic algorithm optimization, classification is carried out. Our research results show that the K nearest neighbor (KNN) as an optimal classification model produces 85.36% accuracy. We also compare the proposed algorithm with the existing algorithms. The experimental results show that the performance of the proposed algorithm is comparable to or better than that of many existing algorithms.

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The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition

IEEE Journal of Biomedical and Health Informatics ◽

10.1109/jbhi.2015.2450196 ◽

2016 ◽

Vol 20 (5) ◽

pp. 1301-1308 ◽

Cited By ~ 37

Author(s):

Gang Wang ◽

Chaolin Teng ◽

Kuo Li ◽

Zhonglin Zhang ◽

Xiangguo Yan

Keyword(s):

Independent Component Analysis ◽

Empirical Mode Decomposition ◽

Component Analysis ◽

Independent Component ◽

Eeg Signals ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition

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Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2017.07.010 ◽

2017 ◽

Vol 88 ◽

pp. 132-141 ◽

Cited By ~ 47

Author(s):

Asmat Zahra ◽

Nadia Kanwal ◽

Naveed ur Rehman ◽

Shoaib Ehsan ◽

Klaus D. McDonald-Maier

Keyword(s):

Empirical Mode Decomposition ◽

Seizure Detection ◽

Eeg Signals ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition

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Physiological Time Series Processing via Empirical Wavelet Transform

Advanced Science Engineering and Medicine ◽

10.1166/asem.2020.2557 ◽

2020 ◽

Vol 12 (5) ◽

pp. 582-587

Author(s):

Omkar Singh

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Empirical Mode Decomposition ◽

Multivariate Data ◽

Multiscale Entropy ◽

Entropy Analysis ◽

Temporal Scales ◽

Physiological Time ◽

Mode Decomposition ◽

Empirical Wavelet Transform

This paper presents the efficacy of empirical wavelet transform (EWT) for physiological time series processing. At first, EWT is applied to multivariate heterogeneous physiological time series. Secondly, EWT is used for the removal of fast temporal scales in multiscale entropy analysis. Empirical mode decomposition is an adaptive data analysis method in the sense that it does not require prior information about the signal statistics and tend to decompose a signal into various constituent modes. The utility of Standard EMD algorithm is however limited to single channel data as it suffers from the problems of mode alignment and mode mixing when applied channel wise for multivariate data. The standard EMD algorithm was extended to multivariate Empirical mode decomposition (MEMD) that can be used analyze a multivariate data. The MEMD can only be applied to multivariate data in which all the channels have equal data length. EWT is another adaptive technique for mode extraction in a signal using empirical scaling and wavelet functions. The multiscale entropy (MSE) algorithm is generally used to quantify the complexity of a time series. The original MSE approach utilizes a coarse-graining process for the removal of fast temporal scales in a time series which is equivalent to applying a finite impulse response (FIR) moving average filter. In Refined Multiscale entropy (RMSE), the FIR filter was replaced with a low pass Butterworth filter which exhibits a better frequency response than that of a FIR filter. In this paper we have presented a new approach for the removal of fast temporal scales based on empirical wavelet transform. The empirical wavelet transform is also used as an innovative filtering approach in multiscale entropy analysis.

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