Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics

Chen, Xun; Liu, Aiping; Chen, Qiang; Liu, Yu; Zou, Liang; McKeown, Martin J.

doi:10.1016/j.compbiomed.2017.06.013

Cited by 42 publications

(26 citation statements)

References 33 publications

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“…The two performance measures reflect two complementary aspects of artifact removal and should always be evaluated simultaneously. Indeed, note that the SER can easily be made infinitely high by using the trivial all-zero filter for W in (11), but then the ARR will reduce to 0 dB.…”

Section: Performance Measuresmentioning

confidence: 99%

“…On the other hand, Canonical Correlation Analysis (CCA) has become popular for its capability to remove muscle artifacts [9], [10]. A joint approach that targets both ocular and muscle artifacts has recently been proposed in [11]. These Blind Source Separation (BSS) methods are intrinsically semi-automatic, as the artifact components need to be selected for removal after source separation.…”

Section: Introductionmentioning

confidence: 99%

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A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

2018

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Section: Performance Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

2018

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“…The Butterworth filter with a cutoff frequency of 4.0 and 45.0 Hz was used to filter the noise in the EEG data [37]. Then, an independent component analysis (ICA) was employed to eliminate muscular artifacts [38]. In each trial, 60-s continuous EEG signals were selected and split into three segments: 3-s baseline segment, 6-s (10%) validating segment, and 54-s working segment.…”

Section: Feature Extraction and The Target Emotion Classesmentioning

confidence: 99%

“…By using a fast Fourier transformation, the frequency features (60 power features, 16 power difference features) were prepared. In each channel, the power features were computed on four frequency bands, i.e., theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Power difference features were employed to detect the variation in cerebral activity between the left and right cortical areas.…”

Section: Feature Extraction and The Target Emotion Classesmentioning

confidence: 99%

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

2019

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Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

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“…To simultaneously extract muscle and ocular artefacts, Chen et al [6] introduced an expanded variant of ICA from one or more datasets. It utilizes information theory principles to divide each set of data into mutually independent sources, while also using a dependency association between sets of data to rely on similar sources across data sets.…”

Section: Introductionmentioning

confidence: 99%

Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

Ahmed

AlShemmary

2020

IEEE Access

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Electrooculogram (EOG) and power line noise artefact detection and rejection have commonly utilized Stone's blind source separation (Stone's BSS) algorithm. The paper suggests a hybrid method between particle swarm optimization (PSO) and Stone's BSS for the detection and rejection of electrooculogram (EOG) and power line noise in the single-channel without the use of a notch filter. The proposed method contains three major steps: centralizing and whitening of the input EEG signal, incorporating the processing EEG signal into the iterative algorithm of the particle swarm optimization (PSO) to randomly generate the optimal value of (hshort, hlong) and weight vector W parameters, and applying Stone's BSS using the generalized eigenvalue decomposition (GEVD) method to eliminate electrooculogram (EOG) and power line noise artefacts to obtain a clean EEG signal. The authors assess the robustness of the suggested method evaluated using real and simulated electroencephalogram (EEG) data sets. The simulated electroencephalogram (EEG) data and electrooculogram (EOG) and line noise (LN) artefacts are produced and mixed randomly in the MATLAB program; two types of real EEG data are taken in 9 and 19 channels. Evaluation results show the proposed algorithms as effective techniques for extracting both the power line noise and electrooculogram (EOG) artefacts from brain mixtures compared to specific BSS algorithms (e.g., Stone's BSS, evolutionary fast independent component analysis (EFICA), fast independent component analysis (FastICA), and joint approximate diagonalization of Eigen matrices (JADE)) while preserving the clinical features of the reconstructed EEG signal.

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Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics

Cited by 42 publications

References 33 publications

A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

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