Removal of Muscle Artifacts From the EEG: A Review and Recommendations

Chen, Xun; Xu, Xueyuan; Liu, Aiping; Lee, Soojin; Chen, Xiang; Zhang, Xu; McKeown, Martin J.; Wang, Z. Jane

doi:10.1109/jsen.2019.2906572

Cited by 90 publications

(44 citation statements)

References 113 publications

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“…The MEMD-IVA method is proposed to remove the muscle artifacts from the acquisition channels, in which the inter-channel interference is also considered one of the noises [60]. Some recommendations are suggested to remove muscle artifacts from a few channel data [64]. All the above methods remove the artifacts effectively by hybrid application.…”

Section: Comparative Analysis Of the Existing Methodsmentioning

confidence: 99%

Artifact Removal Methods in EEG Recordings: A Review

Mathe

Padmaja

Krishna

2021

Proc. eng. technol. innov.

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To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

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Section: Comparative Analysis Of the Existing Methodsmentioning

confidence: 99%

Artifact Removal Methods in EEG Recordings: A Review

Mathe

Padmaja

Krishna

2021

Proc. eng. technol. innov.

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“…EEG mixing approach has many issues, integrating and avoiding the functionally separate and independent source inputs and loss-data information. These sources of information can imply synchronous or partially synchronous conduct in cortical or non-cortical sources [45]. In the ICA decomposition, IC filters using extended-infomax ICA decomposition are implemented to generate the optimally temporarily independent channel EEG signals using the EEGLAB toolbox feature runica [46].…”

Section: B Ica Decomposition and Rejectionmentioning

confidence: 99%

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

et al. 2021

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Recently Noninvasive Electroencephalogram (EEG) systems are gaining much attention. Brain-computer Interface (BCI) systems rely on EEG analysis to identify the mental state of the user, change in cognitive state and response to the events. Motor Execution (ME) is a very important control paradigm. This paper introduces a robust and useful User-Independent Hybrid Brain-computer Interface (UIHBCI) model to classify signals from fourteen EEG channels that are used to record the reactions of the brain neurons of nine subjects. Through this study the researchers identified relevant multisensory features of multi-channel EEG that represent the specific mental processes depending on two different evaluation models (Audio/Video) and (Male/Female). The Deep Belief Network (DBN) was applied independently on the two models where, the overall achieved classification rates were better in ME classification compared to the state of art. For evaluation four models were tested in addition to the proposed model, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Brain-computer Interface Lower-Limb Motor Recovery (BCI LLMR) and Hybrid Steady-State Visual Evoked Potential Rapid Serial Visual Presentation Brain-computer Interface (Hybrid SSVEP-RSVP BCI). Results indicated the proposed model, LDA, SVM, BCI LLMR and Hybrid SSVEP-RSVP BCI accuracies for (A/V) model are 94.44%, 66.67%, 61.11%, 83.33% and 89.67% respectively, while for (M/F) model, the overall accuracies are 94.44%, 88.89%, 83.331%, 85.44% and 89.45%. Finally, the proposed model achieved superiority over the state of art algorithms in both (A/V) and (M/F) models. INDEX TERMS Deep neural networks, independent component analysis, machine learning, motor execution.

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“…These artifacts can affect EEG signals and interfere with relevant or dominant potentials in ERPs. Thus, many previous studies have sought to remove artifacts, such as muscular activity ( Chen et al, 2019 ; Zou et al, 2020 ), cardiac activity ( Hamaneh et al, 2014 ; Dai et al, 2019 ), eyeblinks, and ocular movements ( Dimigen, 2020 ; Egambaram et al, 2020 ). The effect of noise on ERP analysis has been minimized thanks to the development of methods to remove noise in EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

Park

Kim

2021

Front. Physiol.

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The aim of this study was to determine the effect of heartbeat-evoked potentials (HEPs) on the performance of an event-related potential (ERP)-based classification of mental workload (MWL). We produced low- and high-MWLs using a mental arithmetic task and measured the ERP response of 14 participants. ERP trials were divided into three conditions based on the effect of HEPs on ERPs: ERPHEP, containing the heartbeat in a period of 280–700ms in ERP epochs after the target; ERPA-HEP, not including the heartbeat within the same period; and ERPT, all trials including ERPA-HEP and ERPHEP. We then compared MWL classification performance using the amplitude and latency of the P600 ERP among the three conditions. The ERPA-HEP condition achieved an accuracy of 100% using a radial basis function-support vector machine (with 10-fold cross-validation), showing an increase of 14.3 and 28.6% in accuracy compared to ERPT (85.7%) and ERPHEP (71.4%), respectively. The results suggest that evoked potentials caused by heartbeat overlapped or interfered with the ERPs and weakened the ERP response to stimuli. This study reveals the effect of the evoked potentials induced by heartbeats on the performance of the MWL classification based on ERPs.

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Removal of Muscle Artifacts From the EEG: A Review and Recommendations

Cited by 90 publications

References 113 publications

Artifact Removal Methods in EEG Recordings: A Review

Artifact Removal Methods in EEG Recordings: A Review

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

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