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

A new peak fitting method for 1D solid-state 29Si NMR spectra based on singular spectrum analysis

2019 ◽  
Vol 18 (01) ◽  
pp. 1941014
Author(s):  
Guiliang Li ◽  
Changjun Li ◽  
Nan Wei
Keyword(s):  
Molecular Structure ◽  
Spectrum Analysis ◽  
Nmr Spectra ◽  
Singular Spectrum Analysis ◽  
Embedding Dimension ◽  
Characteristic Peak ◽  
Gaussian Fitting ◽  
Singular Spectrum ◽  
Peak Fitting ◽  
Fitting Method

[Formula: see text]Si Nuclear Magnetic Resonance (NMR) can measure the molecular structure of silicate in oilfield reinjection water. However, noise in [Formula: see text]Si NMR spectra (NMRS) affects the determination of silicate molecular structure type. To solve this problem, a new peak fitting method (Two-step Greedy-Singular Spectrum Analysis-Gaussian Fitting Method, TSG-SSA-GFM) is proposed in this paper. This method first uses TSG to determine the embedding dimension, then uses SSA to determine the characteristic peak position. Finally, GFM is used to calculate the molar ratio of characteristic peaks. The results show that TSG can quickly determine the embedding dimension and reduce computation by at least 50% vs. the global ergodic method. The mean deviation of characteristic peak positions determined by SSA is 0.07 ppm, while Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) cannot determine characteristic peaks of [Formula: see text]Si NMRS containing overlapping peak. The average [Formula: see text]-squared of Gaussian fitting of [Formula: see text]Si NMRS is 98.4% while Lorentzian is 90.6%. Therefore, this study provides an important method for quantitative analysis of [Formula: see text]Si NMRS.

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>

Adaptive rank selection for singular spectrum analysis via dip decomposition

2016 ◽  
Author(s):  
Yangkang Chen ◽  
Wei Chen ◽  
Shaohuan Zu ◽  
Weilin Huang ◽  
Dong Zhang
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document