Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG—An efficient approach combining SSA–ICA with wavelet thresholding for BCI applications

Noorbasha, Sayedu Khasim; Sudha, Gnanou Florence

doi:10.1016/j.bspc.2020.102168

Cited by 37 publications

(18 citation statements)

References 22 publications

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“…The current SSA-ICA method could combine the wave-let and other methods and enabled the possibility to conduct ICA in single-channel EEG. In line with several analysis methods of single-channel EEG data [9,16], the current results showed that SSA-ICA could be an optimum one for such application when optimal parameters are applied.…”

Section: Discussionsupporting

confidence: 73%

Source separation on single channel EEG: A pilot study on effect of transcranial alternating current stimulation on scalp meridian

Gao

Liu

Tsang

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Brain electrical stimulation has shown the capability to modulate neural activities in a variety of ways. Compared with transcranial direct current stimulation(tDCS), transcranial alternating current stimulation (tACS) may affect brain activities differently through a frequency-based mechanism. This pilot study applied tACS to the scalp following the meridian (Jingluo) of traditional Chinese medicine to explore its potential neural modulation effect. A wearable electroencephalogram (EEG) device was used to measure the frontal activity in a female participant before and after tACS longitudinally. A combined method of singular spectrum analysis (SSA)-independent components analysis (ICA) was applied to separate potential artifacts from ocular and other irrelevant sources. The results demonstrated that SSA-ICA could effectively separate signals from different sources especially the ocular artifact. EEG spectrum analysis showed that short-term tACS could increase the power of delta waves. This study has good implications for the use of tACS and SSA-ICA method for the study of brain activities. Future research is needed to refine more optimum parameters of tACS and SSA-ICA to make the evidence more solid.Clinical Relevance-tACS may influence the brain wave oscillations through the frequency-based mechanism. SSA-ICA method helps to broaden the use of wearable EEG devices for various clinical applications.

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Section: Discussionsupporting

confidence: 73%

Source separation on single channel EEG: A pilot study on effect of transcranial alternating current stimulation on scalp meridian

Gao

Liu

Tsang

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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“…25 ICA can improve the accuracy of wavelet packet feature extraction. 26 Combining the three methods has complementary effects (as shown in Figure 6).…”

Section: Methodsmentioning

confidence: 99%

A comprehensive experimental framework based on analysis of the pilot’s EEG and NASA-TLX questionnaire in a VR environment

Zhang

et al. 2023

Proc Inst Mech Eng H

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The relationship framework between electroencephalogram (EEG) signals and subjective perception during pilots’ missions was established to improve pilots’ training efficiency and flight safety. Primarily, this study constructs a real flight scene through virtual reality (VR) and then obtains EEG data in simulated scenes. Researchers use VR technology to build a mission simulation room and then acquire EEG data from participants wearing EEG acquisition devices in the simulated room. The experimental process is divided into flight simulation and a questionnaire survey. Based on the participants’ EEG analysis, the researchers verified the changes of β rhythm under a high-difficulty operation mission. In addition, this study infers the mechanism of affecting pilots’ mental workload under high-difficulty operation by analyzing the correlation between subjective questionnaire results and β rhythms. The results showed that in the context of pilots performing flight missions in the aircraft space environment, the pilots’ mental load had the most excellent rhythmic relationship with the regions representing β rhythm. Therefore, a comprehensive experimental framework constructed in this study based on virtual simulation space to analyze the relationship between EEG and NASA-TLX subjective questionnaire provides a set of more accurate reference information for the design of a pilot training system based on pilot training efficiency and flight safety.

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“…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. Researchers have been particularly interested in blind source separation (BSS), which does not require any prior knowledge of EOG and is useful for removing artifacts from multichannel contaminated EEG data [ 15 ].…”

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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Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG—An efficient approach combining SSA–ICA with wavelet thresholding for BCI applications

Cited by 37 publications

References 22 publications

Source separation on single channel EEG: A pilot study on effect of transcranial alternating current stimulation on scalp meridian

Source separation on single channel EEG: A pilot study on effect of transcranial alternating current stimulation on scalp meridian

A comprehensive experimental framework based on analysis of the pilot’s EEG and NASA-TLX questionnaire in a VR environment

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

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