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

Hu, Haowen; Guo, Shengxin; Liu, Ran; Wang, Peng

doi:10.7717/peerj.3474

Cited by 29 publications

(29 citation statements)

References 30 publications

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“…The variation in the length of EEG waves is difficult to study because of disturbances and stochastic noise. Although there are methods for removing artifacts (Teixeira et al 2006, Daly et al 2013, Hu et al 2017, these procedures change the shape of the waves, and this affects the measurements. Therefore, only sequences of waves with a minimal amount of disturbances and noise should be included in a study of the wavelength variation.…”

Section: Methodsmentioning

confidence: 99%

Variation in length of alpha waves reveals how forebrain activity is organized

Johannisson¹

2020

RIO

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Background The cerebral cortex is composed of functional units known as columns. The “two levels” hypothesis states that the activity in a column is either at a high level or at a normal, relatively low level. Measurements indicate that the duration of the high activity in a column is around 450 ms. The number of highly active columns is often 4–5. These data are from previous studies on alpha waves in electroencephalograms. The idea is that alpha waves are created when a regulating system keeps the number of highly active columns within proper limits. If this is true, then regulating signals determines the length of the alpha waves, which opens up for a possibility to test the hypothesis. Methods and results Wavelengths were measured in sequences of alpha waves, and distinctive patterns in the wavelength variation were found. The elements of these patterns were repeated at intervals that exactly matched the predicted duration of high activity in individual columns. Conclusions The discovery of patterns in the wavelength variation confirms the central part of the two levels hypothesis. The patterns reveal the actual number of highly active columns. Moreover, the duration of high activity in a column can be measured. Number and duration outside the optimal ranges may lead to a variety of symptoms.

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Section: Methodsmentioning

confidence: 99%

Variation in length of alpha waves reveals how forebrain activity is organized

Johannisson¹

2020

RIO

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“…In addition to its great success in terms of handling climatic, meteorological, and geophysical data [29], the SSA-based algorithm has been used to analyse EEG signals. Maddirala and Shaik made use of the method based on SSA to eliminate EOG [30] and motion artifacts [31] from EEG in the case of only one channel; Teixeira et al presented an approach to extract high-amplitude artifacts [32]; Hu et al suggested utilizing the method derived from SSA to extract desired brain rhythms [33]. On the basis of these studies, we know that SSA can succeed in separating EEG composed of different sources, which are mixed with each other in the time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Zhang

et al. 2019

Journal of Healthcare Engineering

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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.

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“…Then a new grouping rule based on the local mobility of the eigenvectors was proposed to remove the motion artifact, which performed better than the traditional method [ 19 ]. Based on the similarity of the eigenvalues and the peak frequency of RC, Hai et al proposed another efficient grouping rule enabling SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms [ 25 ]. However, for the issue of embedding dimension selection, to the best of our knowledge, there is no explicit rule for embedding dimension selection.…”

Section: Introductionmentioning

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

Cited by 29 publications

References 30 publications

Variation in length of alpha waves reveals how forebrain activity is organized

Variation in length of alpha waves reveals how forebrain activity is organized

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

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

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