Removal of EMG artifacts from single channel EEG signal using singular spectrum analysis

2015 ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Rafi Ahamed Shaik
Keyword(s):  
Spectrum Analysis ◽  
Single Channel ◽  
Singular Spectrum Analysis ◽  
Eeg Signal ◽  
Singular Spectrum

scholarly journals Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 697 ◽  
Author(s):  
Shanzhi Xu ◽  
Hai Hu ◽  
Linhong Ji ◽  
Peng Wang
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Eeg Signal ◽  
Embedding Dimension ◽  
Singular Spectrum ◽  
Selection For

scholarly journals Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Qingze Liu ◽  
Aiping Liu ◽  
Xu Zhang ◽  
Xiang Chen ◽  
Ruobing Qian ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Spectrum Analysis ◽  
Canonical Correlation ◽  
Single Channel ◽  
Singular Spectrum Analysis ◽  
Eeg Signals ◽  
Singular Spectrum ◽  
Multichannel Eeg ◽  
Channel Decomposition

Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

scholarly journals EMG Artifacts Removal from Multi-Channel EEG Signals using Multi-Channel Singular Spectrum Analysis

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Peak Frequency ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Singular Spectrum ◽  
Emg Signal ◽  
Value Decomposition ◽  
Frequency Variations ◽  
Electroencephalogram Eeg

<div><div><div><p>The Electroencephalogram (EEG) is the brain sig- nals which are most normally debased by Electromyogram (EMG) antiquities. The presence of these EMG antiquities covers the necessary information in an EEG signal. In this paper, we have proposed another strategy named as Multi-channel Singular Spectrum Analysis (MSSA) in light of Singular Value Decomposition (SVD) to expel muscle or EMG antiquities from multi-channel EEG signals. At first, the orthogonal eigenvectors of multi-channel data are estimated by performing SVD which are acquired from the covariance matrix . Since the frequency variations of eigenvectors related to EEG signal are quite low when compared to the EMG signal, so we fix some peak frequency threshold to find out the frequencies related to EEG signal, then the frequencies related to EMG signals are suppressed and the artifact free Multi-channel EEG signal is extracted. Finally, our proposed technique is applied on a noisy sinusoidal signals to test the performance of the proposed method and then it is applied on synthetic EEG signals mixed with the EMG artifacts. Simulation results are then compared with Canonical Correlation Analysis (CCA) to show that the proposed method eliminates EMG antiquities more adequately without amending the required data.</p></div></div></div>

scholarly journals EMG Artifacts Removal from Multi-Channel EEG Signals using Multi-Channel Singular Spectrum Analysis

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Peak Frequency ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Singular Spectrum ◽  
Emg Signal ◽  
Value Decomposition ◽  
Frequency Variations ◽  
Electroencephalogram Eeg

<div><div><div><p>The Electroencephalogram (EEG) is the brain sig- nals which are most normally debased by Electromyogram (EMG) antiquities. The presence of these EMG antiquities covers the necessary information in an EEG signal. In this paper, we have proposed another strategy named as Multi-channel Singular Spectrum Analysis (MSSA) in light of Singular Value Decomposition (SVD) to expel muscle or EMG antiquities from multi-channel EEG signals. At first, the orthogonal eigenvectors of multi-channel data are estimated by performing SVD which are acquired from the covariance matrix . Since the frequency variations of eigenvectors related to EEG signal are quite low when compared to the EMG signal, so we fix some peak frequency threshold to find out the frequencies related to EEG signal, then the frequencies related to EMG signals are suppressed and the artifact free Multi-channel EEG signal is extracted. Finally, our proposed technique is applied on a noisy sinusoidal signals to test the performance of the proposed method and then it is applied on synthetic EEG signals mixed with the EMG artifacts. Simulation results are then compared with Canonical Correlation Analysis (CCA) to show that the proposed method eliminates EMG antiquities more adequately without amending the required data.</p></div></div></div>

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

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