EEG Signal Modeling Using Adaptive Markov Process Amplitude

Al‐Nashash, Hasan; Al-Assaf, Yousef; Paul, Joseph Suresh; Thakor, Nitish V.

doi:10.1109/tbme.2004.826602

Cited by 45 publications

(30 citation statements)

References 13 publications

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“…FT is the basis of spectral analysis, which is reviewed in the next section. Another sinusoidal model of EEG uses the Markov process proposed by Al-Nashash et al (62), which simulates the stationary EEG with k-sinusoidal oscillations:…”

Section: Autoregressive Modelmentioning

confidence: 99%

See 1 more Smart Citation

Advances in Quantitative Electroencephalogram Analysis Methods

Thakor¹,

Tong²

2004

Annu. Rev. Biomed. Eng.

248

137

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Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.

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Section: Autoregressive Modelmentioning

confidence: 99%

“…The coefficients λ j (n) and ξ j (n) are estimated with the help of the least mean square (LMS) algorithm (62). This model can be used to simulate EEG in different conditions.…”

Section: Autoregressive Modelmentioning

confidence: 99%

Advances in Quantitative Electroencephalogram Analysis Methods

Thakor¹,

Tong²

2004

Annu. Rev. Biomed. Eng.

248

137

View full text Add to dashboard Cite

show abstract

“…The spontaneous EEG signal is generated based on the Markov Process Amplitude (MAP) EEG model [ 35 , 36 ]. The rhythmic oscillation is represented by the sinusoidal wave, while the stochastic process is represented by the first-order Markov process.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

et al. 2018

Sensors

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The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

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“…In the MPA EEG model, the EEG rhythmic oscillations were represented by sinusoidal waves, and the EEG randomness was represented by the stochastic process amplitude of the first-order Markov process (Al-Nashash et al, 2004; Bai et al, 2000; Bai et al, 2001). The spontaneous EEG was generated by a combination of K different oscillations, where n is the sample number, Δ t is the time interval, K is the number of rhythms, m is the dominant frequency, θ is an arbitrary value representing the initial phase, a is the rhythmic amplitude obtained from a first-order Gauss–Markov process where γ is the coefficient of the first-order Markov process, ξ is a random increment of Gaussian distribution with zero mean and variance σ .…”

Section: Methodsmentioning

confidence: 99%

An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

Guo

Liu

et al. 2017

PeerJ

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Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).

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EEG Signal Modeling Using Adaptive Markov Process Amplitude

Cited by 45 publications

References 13 publications

Advances in Quantitative Electroencephalogram Analysis Methods

Advances in Quantitative Electroencephalogram Analysis Methods

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

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