Removal of EOG artifacts from single channel EEG – An efficient model combining overlap segmented ASSA and ANC

Noorbasha, Sayedu Khasim; Sudha, Gnanou Florence

doi:10.1016/j.bspc.2020.101987

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…This consists of 23 records, each with two single channel signals [24], with 2048 Hz sampling frequency [25]. As per [26], high sampling rate involves more computations. Hence, to avoid it, these signals are down sampled to 250 Hz.…”

Section: Data Generationmentioning

confidence: 99%

“…In [10] conjunction with ICA, Discrete Wavelet Transform (DWT) eliminates artifacts from the single EEG channel. Its success depends, however, on the right choice of the decomposition stages and the mother wavelet, which is a difficult task [11]. To avoid this difficulty, ensemble empirical mode decomposition (EEMD) is combined with ICA in [12] and CCA in [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid algorithm for multi artifact removal from single channel EEG

Noorbasha

Sudha

2021

Biomed. Phys. Eng. Express

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Electroencephalogram (EEG) signals recorded from the ambulatory systems are mostly contaminated by various artifacts like, electrooculogram (EOG), motion artifacts (MA) and electrocardiogram (ECG) artifacts. These artifacts limit the accuracy in further analysis of EEG in practise. So far, several existing methods have been proposed with the combination of decomposition techniques and independent component analysis (ICA) to remove single artifacts and only few methods to remove multiple artifacts from the single channel EEG. As improperly denoised EEG signals can result in wrong diagnosis, in this work, Singular Spectrum Analysis (SSA) and ICA are jointly combined with Generalized Moreau Envelope Total Variation (GMETV) technique to simultaneously remove combinations of different artifacts from single channel EEG. In this work, the SSA is used to decompose the contaminated single channel EEG, while the ICA is employed to separate the various hidden sources as independent components (ICs). Although the ICA is adequate in source separation, there is still, some essential EEG signal data appearing as artifact in the IC. Hence, eliminating this would allow EEG signal information to be lost. The GMETV approach is proposed in this paper, to estimate the actual artifacts in order to address these issues. The estimated actual artifacts are subtracted from the artifact ICs providing the residue of wanted component of EEG. This residue is added back to the remaining ICs, to obtain the denoised EEG. Simulation results demonstrated that the proposed technique performs better compared to the existing techniques. The Relative Root Mean Square Error (RRMSE) is reduced by 12.02% and 7.22% compared to SSA-ICA and SSA-ICA-thresholding respectively. Similarly, the Correlation Coefficient (CC) is increased by 21.48% and 8.25% with respect to SSA-ICA and SSA-ICA-thresholding respectively.

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Section: Data Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid algorithm for multi artifact removal from single channel EEG

Noorbasha

Sudha

2021

Biomed. Phys. Eng. Express

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“…In Mannan et al [ 10 , 11 , 12 ], various single and multi-channel approaches were established. By combining overlap segmented adaptive singular spectrum analysis (Ov-ASSA) and adaptive noise canceler (ANC), Noorbasha et al [ 13 ] established a single channel artifact elimination system with clear improvements in epilepsy identification. A unique approach, designated as singular spectrum analysis, independent component analysis, and stationary wavelet transform (SSA-ICA-SWT) [ 14 ], was introduced according to the combination of SSA and ICA with a SWT, and it produced the good artifact elimination work different to current methods such as SSA, SSA-ANC, and SSA-ICA.…”

Section: Introductionmentioning

confidence: 99%

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

Yedukondalu

Sharma

2023

Sensors

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Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

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“…In [ 38 ], the SSA is combined with an adaptive filter to enhance the performance of the adaptive filter over the method in [ 27 ]. Recently, in [ 39 ], with new grouping criteria, the adaptive SSA technique is combined with ANC (SSA+ANC) and the method showed better performance over the method in [ 38 ]. Moreover, SSA is used as a means to apply ICA on single-channel EEG signals [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Maddirala

Veluvolu

2022

Sensors

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Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

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Removal of EOG artifacts from single channel EEG – An efficient model combining overlap segmented ASSA and ANC

Cited by 16 publications

References 15 publications

Hybrid algorithm for multi artifact removal from single channel EEG

Hybrid algorithm for multi artifact removal from single channel EEG

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

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