Independent Vector Analysis Applied to Remove Muscle Artifacts in EEG Data

Chen, Xun; Peng, Hu; Yu, Fengqiong; Wang, Kai

doi:10.1109/tim.2016.2608479

Cited by 82 publications

(41 citation statements)

References 38 publications

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“…A popular method for eye blinking artifact detection is regression [10,11]. Blind source separation (BSS) methodologies like independent component analysis (ICA) [4,5,12] and independent vector analysis [13] are often used for ocular, muscular, and cardiac artifact rejection. Once the decomposition has been achieved, components corresponding to artifact activity may be rejected manually or using automatic methods [14].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

Saifutdinova

Congedo

Dudysová

et al. 2019

Sensors

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In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

Saifutdinova

Congedo

Dudysová

et al. 2019

Sensors

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“…The weight modernized equation can be written as x(n+1) = x(n) + V(n) sgn{s(n)} {r(n)} (12) x(n+1) = x(n) + V(n) {s(n)} sgn{r(n)} (13) x(n+1) = x(n) + V(n) sgn{s(n)} sgn{r(n)} (14) The further requirements are to calculate the V(n) from the equations (12) -(14) can be minimized by utilizing block-based algorithms, in this the primary key is decomposed into blocks and each block consists of high extent is utilized to calculate V(n). With this the weight modernized equation can be written by considering the equations (12) - (14) and then S Li = 0 and f=0 exhibits the following form, x(n+1) = x(n) + sgn{s(n)} {r(n)} (15) x(n+1) = x(n) + {s(n)} sgn{r(n)} (16) and…”

Section: Fig 1 Configuration Of Adaptive Noise Eliminatormentioning

confidence: 99%

Artifact Elimination in EEG Signal using Block and Sign Based Normalized Least Mean Square Techniques

Soniya¹,

Goduguluri²

2019

IJITEE

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In this research the efficient and low computation complex signal acclimatizing techniques are projected for the improvement of Electroencephalogram (EEG) signal in remote health care applications. In clinical practices the EEG signal is extracted along with the artifacts and with some small constraints. Mainly in remote health care situations, we used low computational complexity filters which are striking. So, for the improvement of the EEG signal we introduced efficient and computation less Adaptive Noise Eliminators (ANE’s). These techniques simply utilize addition and shift operations, and also reach the required convergence speed among the other predictable techniques. The projected techniques are executed on real EEG signals which are stored and are compared with the effecting EEG arrangement. Our realizations visualize that the projected techniques offer the best concert over the previous techniques in terms of signal to noise ratio, mathematical complexity, convergence rate, Excess Mean Square error and Mis adjustment. This approach is accessible for the brain computer interface applications.

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“…2, it can be observed that the MEMD-IVA performed clearly better than other methods according to the performance metrics both relative root mean square error (RRMSE) and average correlation coefficient (ACC) for all the SNR values. The definition of RRMSE and ACC can be found in [8]. Results from real data: Fig.…”

Section: Data Generation and Acquisitionmentioning

confidence: 99%

Removal of muscle artefacts from few‐channel EEG recordings based on multivariate empirical mode decomposition and independent vector analysis

Chen

2018

Electron. lett.

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Electroencephalography (EEG) recordings are often contaminated by muscle artefacts. To address the problem, various methods have been proposed to suppress muscle artefacts from either multichannel or single-channel EEG recordings. However, there exist few studies for muscle artefact removal from few-channel EEG recordings. An effective solution for the few-channel situation by combining multivariate empirical mode decomposition (MEMD) with independent vector analysis (IVA), termed as MEMD-IVA, is proposed. The proposed method consists of two steps. MEMD is first utilised to decompose a few-channel EEG recording into intrinsic mode functions (IMFs) and then IVA is applied on the IMFs to separate sources related to muscle activity. The performance of the proposed method on simulated and real-life data is evaluated. The results demonstrated that MEMD-IVA outperforms other possible existing methods in a few-channel situation.

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Independent Vector Analysis Applied to Remove Muscle Artifacts in EEG Data

Cited by 82 publications

References 38 publications

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

Artifact Elimination in EEG Signal using Block and Sign Based Normalized Least Mean Square Techniques

Removal of muscle artefacts from few‐channel EEG recordings based on multivariate empirical mode decomposition and independent vector analysis

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