Performance Evaluation of Empirical Mode Decomposition for EEG Artifact Removal

2017 ◽  
Author(s):  
MD Erfanul Alam ◽  
Biswanath Samanta
Keyword(s):  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Fluorescent Light ◽  
Eeg Signals ◽  
Signal Processing Technique ◽  
Mode Decomposition

Electroencephalography measures the sum of the post-synaptic potentials generated by many neurons having the same radial orientation with respect to the scalp. The electroen-cephalographic signals (EEG) are weak and often contaminated with different artifacts that have biological and external sources. Reliable pre-processing of the noisy, non-linear, and non-stationary brain activity signals is needed for successful extraction of characteristic features in motor imagery based brain-computer interface (MI-BCI). In this work, a signal processing technique, namely, empirical mode decomposition (EMD), has been proposed for processing EEG signals acquired from volunteer subjects for characterization and identification of motor imagery (MI) activities. EMD has been used for removal of artifacts like electrooculography (EOG) that strongly appears in frontal electrodes of EEG and the power line noise that is mainly produced by the fluorescent light. The performance of EMD has been compared with two extensions, ensemble empirical mode decomposition (EEMD) and multivariate empirical mode decomposition (MEMD)using signal to noise ratio (SNR). The maximum SNR values found for EMD, EEMD and MEMD are 4.30, 7.64 and 10.62 respectively for the EEG signals considered.

FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 25 (06) ◽  
pp. 1350058 ◽  
Author(s):  
Pablo F. Diez ◽  
Vicente A. Mut ◽  
Eric Laciar ◽  
Abel Torres ◽  
Enrique M. Avila Perona
Keyword(s):  
Empirical Mode Decomposition ◽  
Communication System ◽  
Brain Activity ◽  
Features Extraction ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Machine Communication ◽  
Mental Tasks

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

scholarly journals Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation

2018 ◽  
Vol 50 (2) ◽  
pp. 498-516 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Ozgur Kisi ◽  
Lloyd H. C. Chua
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Support Vector ◽  
Pan Evaporation ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Testing Stage ◽  
Adaptive Time ◽  
Two Hybrid

Abstract Accurate prediction of pan evaporation (PE) is one of the crucial factors in water resources management and planning in agriculture. In this research, two hybrid models, self-adaptive time-frequency methodology, ensemble empirical mode decomposition (EEMD) coupled with support vector machine (EEMD-SVM) and EEMD model tree (EEMD-MT), were employed to forecast monthly PE. The EEMD-SVM and EEMD-MT were compared with single SVM and MT models in forecasting monthly PE, measured between 1975 and 2008, at Siirt and Diyarbakir stations in Turkey. The results were evaluated using four assessment criteria, Nash–Sutcliffe Efficiency (NSE), root mean square error (RMSE), performance index (PI), Willmott's index (WI), and Legates–McCabe's index (LMI). The EEMD-MT model respectively improved the accuracy of MT by 36 and 44.7% with respect to NSE and WI in the testing stage for the Siirt station. For the Diyarbakir station, the improvements in results were less spectacular, with improvements in NSE (1.7%) and WI (2.2%), respectively, in the testing stage. The overall results indicate that the proposed pre-processing technique is very promising for complex time series forecasting and further studies incorporating this technique are recommended.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

scholarly journals Removal of Baseline Wander from Electrocardiogram using Ensemble Empirical Mode Decomposition and Low Pass Filter

2019 ◽  
Vol 8 (4) ◽  
pp. 2771-2774
Keyword(s):  
Empirical Mode Decomposition ◽  
Human Heart ◽  
Signal To Noise Ratio ◽  
Normal Sinus Rhythm ◽  
Ensemble Empirical Mode Decomposition ◽  
Comparative Performance ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Mode Decomposition ◽  
Baseline Wander

Electrocardiogram (ECG) is a graphical visualization of the electrical activity of human heart. The biomedical signal, such as ECG, has a major issue of separating the pure signal from artifacts due to baseline wander (BW), electrode artifacts, muscle artifacts, and power-line interference. Reduction of these artifacts is vital for clinical purposes for diagnosis and interpretation of the human heart condition. This paper presents removal of BW from ECG using ensemble empirical mode decomposition (EMD) with multiband filtering approach. A comparative performance analysis of EMD and ensemble EMD for synthetic as well as real BW on normal sinus rhythm and arrhythmia ECG signal are presented. This method can remove the BW in different inherent signal to noise ratio (SNR) including negative and positive as well. This method shows that quantitative and qualitative results with miniscule signal distortion via experiments on several ECG records.

scholarly journals ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 12 (2) ◽  
pp. 2382-2389
Author(s):  
Rajesh Birok, Et. al.
Keyword(s):  
Neural Networks ◽  
Empirical Mode Decomposition ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Novel Approach ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Art Methods

Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc.  Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morphology of the ECG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and ANN. The use of CEEMD ensures that the Neural Network does not get over fitted. It also significantly helps in building better predictors at individual frequency levels. The proposed method is compared with other state-of-the-art methods in terms of Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Correlation Coefficient. The results show that the proposed method has better performance as compared to other state-of-the-art methods for low frequency artifacts removal from EEG.  

scholarly journals Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research

2022 ◽  
Vol 16 ◽  
pp. 509-516
Author(s):  
Wei Li ◽  
Wei Hu ◽  
Kun Hu ◽  
Qiang Qin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Electrical Signal ◽  
Signal Acquisition ◽  
Semg Signal ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Human Muscles ◽  
Eemd Method

The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.

scholarly journals Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 66-74
Keyword(s):  
Brain Activity ◽  
Complete Information ◽  
Ensemble Empirical Mode Decomposition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
The Comparative Study

The electroencephalogram (EEG) is a test that determines brain activity. The existence of artifacts in EEG can naturally decrease the smoothness of the analysis of the biomedical signal. EEG disturbed by noises during encephalogram recordings is one of the problems that the experts have to investigate for finding solutions to remove these artifacts. To obtain an accurate EEG signal; the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used and compared with and its old versions. The non-stationarity and non-linearity of the EEG signal cannot provide complete information when using the traditional method (Temporal and frequency domains). Among the objectives is the comparative analysis of time-frequency distributions applied on EEG signals. The denoising and time-frequency methods used in this study are tested on healthy and abnormal EEG signals which are disturbed by natural and artificial noises. The comparative study in this work shows the effectiveness of the combination of the ICEEMDAN and Periodogram methods that are suitable for denoising and analyzing the EEG signal.

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