ReMAE: User-Friendly Toolbox for Removing Muscle Artifacts From EEG

Chen, Xun; Liu, Qingze; Tao, Weiliang; Li, Luchang; Lee, Soojin; Liu, Aiping; Chen, Qiang; Cheng, Juan; McKeown, Martin J.; Wang, Z. Jane

doi:10.1109/tim.2019.2920186

Cited by 39 publications

(19 citation statements)

References 99 publications

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“…The method was shown to be suitable in detecting artifacts from recordings collected with an easy-to-use electrode and device suitable for demanding clinical environment such as ICU where electrode attachment with minimum preparation is highly appreciated [25]. [26,27]. According to the figure, independent component analysis (ICA) is the most commonly used technique among all the approaches and also among most of EEG processing toolboxes which already been developed.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

Bahador

Erikson²,

Laurila³

et al. 2020

J. Neural Eng.

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Objective: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.Approach: A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact.Main results: The method with a classification accuracy of 92.30% (AUC=0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset.Significance: This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

Bahador

Erikson²,

Laurila³

et al. 2020

J. Neural Eng.

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“…Next, independent component analysis (ICA) was used to remove artifacts such as electromyography and electrooculogram. ReMAE was used to further remove EMG artifacts from EEG signals ( Chen et al, 2020 ). EEG and sEMG data in the beta (14–30 Hz) and gamma bands (31–45 Hz) were selected for further analysis.…”

Section: Methodsmentioning

confidence: 99%

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

et al. 2021

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As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.

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“…ICA-based artifact reduction techniques have been widely used in the field of EEG signal processing because of their powerful signal separation accuracy, simplicity (low computational cost), and ease of use (Delorme et al, 2007;Dimigen, 2019;Jiang et al, 2019). The techniques for limiting ocular and muscular artifacts (Chen et al, 2019;Tian et al, 2020) other than the ICA family are useful if they are integrated in a cascadetype processing module, which can automatically identify the type of artifact contained in the EEG observation. A simple filtering (linear combination) approach such as ICA, which multiplies the demixing matrix W as a filter, is faster and userfriendly.…”

Section: Automatic Processing Architecturementioning

confidence: 99%

“…The performances of proposed artifact reduction techniques in most previous studies were evaluated and ranked based on a metric (e.g., signal-to-noise ratio and correlation) that indicates how the signal quality of the estimated neuronal sources was preserved (Islam et al, 2016). We do not know the original (true) neuronal sources of EEG observations; thus, synthetic data whose pseudoneuronal/pseudo-artifactual sources and mixing process are known were usually used to calculate the metric (Chen et al, 2019;Mucarquer et al, 2019). After the quantified evaluation of signal quality in the estimated sources through the proposed artifact reduction technique, the separation ability for real data is qualitatively shown (Blum et al, 2019;Kanoga et al, 2019a).…”

Section: Efficacy Of Artifact Reduction For Bcismentioning

confidence: 99%

“…In such situation, the performance of artifact reduction techniques will change and might be decreased. Recent studies attempt to propose a generic artifact removal algorithm (Chen et al, 2019). Unlike the time-domain algorithm, frequency-domain methods (i.e., IVA and ILRMA) can separate single-channel data if the differences in data-driven spectral basis functions can be learned well (Kanoga and Mitsukura, 2014;Kanoga et al, 2019a).…”

Section: Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Independent Low-Rank Matrix Analysis-Based Automatic Artifact Reduction Technique Applied to Three BCI Paradigms

Kanoga

Hoshino

Asoh

2020

Front. Hum. Neurosci.

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ReMAE: User-Friendly Toolbox for Removing Muscle Artifacts From EEG

Cited by 39 publications

References 99 publications

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

Independent Low-Rank Matrix Analysis-Based Automatic Artifact Reduction Technique Applied to Three BCI Paradigms

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