ICA-based reduction of electromyogenic artifacts in EEG data: Comparison with and without EMG data

Gabsteiger, Florian; Leutheuser, Heike; Reis, Pedro; Lochmann, Matthias; Eskofier, Bjoern M.

doi:10.1109/embc.2014.6944466

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Further, including electromyography (EMG) recordings may help to improve artifact attenuation (Gabsteiger et al 2014). Yet, in previous studies it was possible to sufficiently attenuate movementrelated artifacts using ICA without additional information from EMG channels (Debener et al 2012, Salvidegoitia et al 2019, Scanlon et al 2020.…”

Section: Auditory Attention Decoding (Aad)-distractionmentioning

confidence: 99%

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG

et al. 2021

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Objective. Neuro-steered assistive technologies have been suggested to offer a major advancement in future devices like neuro-steered hearing aids. Auditory attention decoding methods would in that case allow for identification of an attended speaker within complex auditory environments, exclusively from neural data. Decoding the attended speaker using neural information has so far only been done in controlled laboratory settings. Yet, it is known that ever-present factors like distraction and movement are reflected in the neural signal parameters related to attention. Approach. Thus, in the current study we applied a two-competing speaker paradigm to investigate performance of a commonly applied EEG-based auditory attention decoding (AAD) model outside of the laboratory during leisure walking and distraction. Unique environmental sounds were added to the auditory scene and served as distractor events. Main results. The current study shows, for the first time, that the attended speaker can be accurately decoded during natural movement. At a temporal resolution of as short as 5-seconds and without artifact attenuation, decoding was found to be significantly above chance level. Further, as hypothesized, we found a decrease in attention to the to-be-attended and the to-be-ignored speech stream after the occurrence of a salient event. Additionally, we demonstrate that it is possible to predict neural correlates of distraction with a computational model of auditory saliency based on acoustic features. Conclusion. Taken together, our study shows that auditory attention tracking outside of the laboratory in ecologically valid conditions is feasible and a step towards the development of future neural-steered hearing aids.

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Section: Auditory Attention Decoding (Aad)-distractionmentioning

confidence: 99%

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG

et al. 2021

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“…As mentioned in the introduction, automated rejection is not necessarily unique to ERASE (Delorme et al, 2001 , 2007 ; Nicolaou and Nasuto, 2007 ; Nolan et al, 2010 ; Mognon et al, 2011 ; Daly et al, 2012 , 2013 ; Wu et al, 2018 ; Vaidya et al, 2019 ), given that other methods, such as cICA can also involve automatic IC rejection when prior knowledge of EMG signals is available (Hesse and James, 2006 ; Akhtar et al, 2012 ; Urigüen and Garcia-Zapirain, 2015 ). Previously reported EMG artifacts removal methods also proposed automated rejection techniques, in which some classifiers were built to classify the ICs into EMG sources and EEG sources based on ICs statistical features (Nolan et al, 2010 ; Gabsteiger et al, 2014 ; Wu et al, 2018 ). However, one unique aspect of ERASE compared to these prior reports is that rejection criteria are based on physiological features of both EEG and EMG for automated EMG artifacts rejection procedure (section 2.1.2), which makes ERASE more focused on preserving relevant EEG phenomenon.…”

Section: Discussionmentioning

confidence: 99%

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)—A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG

Wang

Vaidya

et al. 2021

Front. Neurosci.

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Electroencephalographic (EEG) recordings are often contaminated by electromyographic (EMG) artifacts, especially when recording during movement. Existing methods to remove EMG artifacts include independent component analysis (ICA), and other high-order statistical methods. However, these methods can not effectively remove most of EMG artifacts. Here, we proposed a modified ICA model for EMG artifacts removal in the EEG, which is called EMG Removal by Adding Sources of EMG (ERASE). In this new approach, additional channels of real EMG from neck and head muscles (reference artifacts) were added as inputs to ICA in order to “force” the most power from EMG artifacts into a few independent components (ICs). The ICs containing EMG artifacts (the “artifact ICs”) were identified and rejected using an automated procedure. ERASE was validated first using both simulated and experimentally-recorded EEG and EMG. Simulation results showed ERASE removed EMG artifacts from EEG significantly more effectively than conventional ICA. Also, it had a low false positive rate and high sensitivity. Subsequently, EEG was collected from 8 healthy participants while they moved their hands to test the realistic efficacy of this approach. Results showed that ERASE successfully removed EMG artifacts (on average, about 75% of EMG artifacts were removed when using real EMGs as reference artifacts) while preserving the expected EEG features related to movement. We also tested the ERASE procedure using simulated EMGs as reference artifacts (about 63% of EMG artifacts removed). Compared to conventional ICA, ERASE removed on average 26% more EMG artifacts from EEG. These findings suggest that ERASE can achieve significant separation of EEG signal and EMG artifacts without a loss of the underlying EEG features. These results indicate that using additional real or simulated EMG sources can increase the effectiveness of ICA in removing EMG artifacts from EEG. Combined with automated artifact IC rejection, ERASE also minimizes potential user bias. Future work will focus on improving ERASE so that it can also be used in real-time applications.

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“…However, these methods have some limitations, mainly related to the inability to completely remove noise from the corrupted signal without the introduction of undesired distortions, and the need for a priori noise information for signal filtering. These limitations, associated with several features estimated from the EEG signal to suit the diversity of applications, motivate the search for multiple gold standards for removing EEG artifacts (Safieddine et al, 2012 ; Gabsteiger et al, 2014 ; Urigüen and Garcia-Zapirain, 2015 ; Bono et al, 2016 ; Upadhyay et al, 2016 ; Frølich and Dowding, 2018 ; Mucarquer et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography

Queiroz

Silva

Walter

et al. 2022

Front. Comput. Neurosci.

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Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.

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ICA-based reduction of electromyogenic artifacts in EEG data: Comparison with and without EMG data

Cited by 6 publications

References 18 publications

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG

Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)—A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG

Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography

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