Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal
<p>The coefficients <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ζ</mi> <mrow> <mi mathvariant="normal">p</mi> <mo>,</mo> <mi mathvariant="normal">q</mi> </mrow> </msub> </mrow> </semantics> </math> for different parameters of L and p: (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">L</mi> <mn>1</mn> </msub> </mrow> </semantics> </math> = 20; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="normal">L</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> = 50.</p> "> Figure 2
<p>The simulation process of the spontaneous electroencephalography (EEG) signal: (<b>a</b>) simulated delta, theta, alpha and beta rhythm; (<b>b</b>) the spontaneous EEG signal constructed by the superposition of the four rhythms.</p> "> Figure 3
<p>Power spectrum density (PSD) of the first six reconstructed components for the spontaneous EEG signal: (<b>a</b>) L = 40; (<b>b</b>) L = 80.</p> "> Figure 4
<p><math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ε</mi> <mrow> <mi>ave</mi> </mrow> </msub> </mrow> </semantics> </math> corresponding to different embedding dimensions L during the alpha rhythm extraction from the spontaneous EEG signal.</p> "> Figure 5
<p>Extracting the alpha rhythm from the simulated EEG signal by different embedding dimensions L: (<b>a</b>) the simulated EEG signal; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">ε</mi> <mrow> <mi>ave</mi> </mrow> </msub> </mrow> </semantics> </math> corresponding to different embedding dimensions L.</p> "> Figure 6
<p>PSD of the first six reconstructed components for the simulated EEG signal: (<b>a</b>) L = 40; (<b>b</b>) L = 80.</p> "> Figure 6 Cont.
<p>PSD of the first six reconstructed components for the simulated EEG signal: (<b>a</b>) L = 40; (<b>b</b>) L = 80.</p> "> Figure 7
<p>Extracted alpha rhythm: (<b>a</b>) L = 20; (<b>b</b>) L = 40; (<b>c</b>) L = 80; (<b>d</b>) PSD of the extracted alpha rhythm by different embedding dimensions L, as compared with that of the simulated alpha rhythm.</p> "> Figure 8
<p>The real EEG signal and corresponding PSD under eyes-closed condition: (<b>a</b>) the real EEG signal under eyes-closed condition; (<b>b</b>) PSD of the real EEG signal under eyes-closed condition.</p> "> Figure 9
<p>PSD of the first nine reconstructed components of the real EEG signal: (<b>a</b>) L = 20; (<b>b</b>) L = 40; (<b>c</b>) L = 80.</p> "> Figure 9 Cont.
<p>PSD of the first nine reconstructed components of the real EEG signal: (<b>a</b>) L = 20; (<b>b</b>) L = 40; (<b>c</b>) L = 80.</p> "> Figure 10
<p>Extracted alpha rhythm under eyes-closed condition: (<b>a</b>) L = 20; (<b>b</b>) L = 40; (<b>c</b>) L = 80; (<b>d</b>) PSD of the extracted alpha rhythm by different L.</p> "> Figure 10 Cont.
<p>Extracted alpha rhythm under eyes-closed condition: (<b>a</b>) L = 20; (<b>b</b>) L = 40; (<b>c</b>) L = 80; (<b>d</b>) PSD of the extracted alpha rhythm by different L.</p> "> Figure 11
<p>Extracted alpha rhythm under eyes-open condition: (<b>a</b>) L = 20; (<b>b</b>) L = 40; (<b>c</b>) L = 80; (<b>d</b>) PSD of the extracted alpha rhythm by different L.</p> "> Figure 11 Cont.
<p>Extracted alpha rhythm under eyes-open condition: (<b>a</b>) L = 20; (<b>b</b>) L = 40; (<b>c</b>) L = 80; (<b>d</b>) PSD of the extracted alpha rhythm by different L.</p> "> Figure 12
<p>Spectrogram of the extracted alpha rhythm under eyes-open and eye-closed conditions by L = 40.</p> "> Figure 13
<p>Extracted alpha rhythm by wavelet decomposition (WDec) under: (<b>a</b>) eyes-closed condition; (<b>b</b>) eyes-open condition.</p> "> Figure 14
<p>Extracted alpha rhythm by infinite impulse response (IIR) under: (<b>a</b>) eyes-closed condition; (<b>b</b>) eyes-open condition.</p> "> Figure 15
<p>Extracted alpha rhythm by SSA# under: (<b>a</b>) eyes-closed condition; (<b>b</b>) eyes-open condition.</p> "> Figure 16
<p>PSD of the extracted alpha rhythm by four methods: (<b>a</b>) under eyes-closed condition; (<b>b</b>) under eyes-open condition.</p> "> Figure 17
<p>Classification results between eyes-closed and eyes-open states by: (<b>a</b>) adaptive SSA; (<b>b</b>) WDec; (<b>c</b>) IIR; (<b>d</b>) SSA#; (<b>e</b>) autoregressive model (AR).</p> "> Figure 17 Cont.
<p>Classification results between eyes-closed and eyes-open states by: (<b>a</b>) adaptive SSA; (<b>b</b>) WDec; (<b>c</b>) IIR; (<b>d</b>) SSA#; (<b>e</b>) autoregressive model (AR).</p> ">
Abstract
:1. Introduction
2. Adaptive Singular Spectrum Analysis Method for EEG Processing
3. Simulation Results and Discussion
3.1. Markov Process Amplitude EEG Model
3.2. Adaptive Singular Spectrum Analysis for Simulated EEG signal
4. Experimental Results and Discussion
4.1. Measurement Setup and Experimental Procedure
4.2. Adaptive Singular Spectrum Analysis for Real EEG Signal
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Symbol | Value | Comment | |
---|---|---|---|
Spontaneous EEG | /Hz | 2.50 | Delta rhythm |
0.99 | |||
2.26 | |||
/Hz | 6.00 | Theta rhythm | |
0.97 | |||
2.78 | |||
/Hz | 10.50 | Alpha rhythm | |
0.99 | |||
2.35 | |||
/Hz | 21.50 | Beta rhythm | |
0.99 | |||
0.36 | |||
Artifacts | / | 50 | Amplitude of EOG |
/s | 3 | Period of EOG | |
/s | 0.3 | Pulse width of EOG | |
/ | 10 | Amplitude of baseline drift | |
/Hz | 0.5 | Frequency of baseline drift | |
Noise | /dBW | 1 | Power of white noise |
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Xu, S.; Hu, H.; Ji, L.; Wang, P. Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal. Sensors 2018, 18, 697. https://doi.org/10.3390/s18030697
Xu S, Hu H, Ji L, Wang P. Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal. Sensors. 2018; 18(3):697. https://doi.org/10.3390/s18030697
Chicago/Turabian StyleXu, Shanzhi, Hai Hu, Linhong Ji, and Peng Wang. 2018. "Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal" Sensors 18, no. 3: 697. https://doi.org/10.3390/s18030697