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

Somers, Ben; Francart, Tom; Bertrand, Alexander

doi:10.1088/1741-2552/aaac92

Cited by 233 publications

(203 citation statements)

References 27 publications

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“…In Das et al (2016), it has been shown that this data resulted in significantly better attention decoding performance in comparison to dichotically presented unfiltered stimuli. The multi-channel Wiener filtering (MWF) method in Somers et al (2018) was used on the EEG data for artifact removal. The EEG data was bandpass filtered between 1 and 9 Hz.…”

Section: Validation Experimentsmentioning

confidence: 99%

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Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Das

Vanthornhout

Francart

et al. 2019

Preprint

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A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using noninvasive techniques like magneto-or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to inform the design of a spatial filter that projects the data onto high-SNR directions. However, collecting enough repeated trials is often impractical and even impossible in some paradigms. Therefore, we present a data-driven spatial filter design that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method uses the stimulus-driven neural response, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus, our method resulted in better short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.

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Section: Validation Experimentsmentioning

confidence: 99%

“…Preprocessing of the EEG data was done similar to Vanthornhout et al (2018). The EEG was highpass filtered (second order Butterworth with cut-off at 0.5 Hz) and downsampled to 256 Hz before applying the MWF for artifact rejection (Somers et al, 2018). It was then re-referenced to the Cz electrode (therefore C = 63).…”

Section: Validation Experimentsmentioning

confidence: 99%

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Das

Vanthornhout

Francart

et al. 2019

Preprint

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“…The EEG data was downsampled from 8192 Hz to 256 Hz similarly to the envelope. Next, a generic EEG artifact removal algorithm (Somers et al, 2018) was applied on the EEG-data to remove eye artifacts similar to described in Decruy et al (2019). EEG-signals were then re-referenced to Cz and bandpass filtered for delta-and theta-band using the same Chebyshev filter as used for the envelope.…”

Section: Signal Processing: Envelope Reconstructionmentioning

confidence: 99%

“…The EEG was first downsampled from 8192 Hz to 512 Hz, similar to Bernarding et al (2014). Then artifacts were removed using a generic EEG artifact removal algorithm (Somers et al, 2018). Consequently, EEG signals were re-referenced to Cz and bandpass filtered using the same Chebyshev filter as used for envelope reconstruction.…”

Section: Signal Processing: Phase Synchronization and Alpha Powermentioning

confidence: 99%

Top-down modulation of neural envelope tracking: the interplay with behavioral, self-report and neural measures of listening effort

Decruy

Lesenfants

Vanthornhout

et al. 2019

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AbstractWhen listening to natural speech, our neural activity tracks the speech envelope. Moreover, recent research has demonstrated that this neural envelope tracking can be affected by top-down processes. The present study was designed to examine if neural envelope tracking is modulated by the effort that a person expends during listening. Five measures were included to quantify listening effort: two behavioral measures based on a novel dual-task paradigm, a self-report effort measure and two neural measures related to neural phase synchronization and alpha power. Electroencephalography responses to sentences, presented at a wide range of subject-specific signal-to-noise ratios, were recorded in thirteen young, normal-hearing adults. A comparison of the five measures revealed different effects of listening effort as a function of speech understanding. Reaction times on the primary task and self-reported effort decreased with increasing speech understanding. In contrast, reaction times on the secondary task and alpha power showed a peak-shaped behavior with highest effort at intermediate speech understanding levels. We found a positive association between envelope tracking and speech understanding. While a significant effect of listening effort was found on theta-band envelope tracking, the effect size was negligible. Therefore, our results suggest that listening effort is not a confound when using envelope tracking to objectively measure speech understanding in young, normal-hearing adults.

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“…Electroencephalography (EEG), for example, is a portable neuroimaging system that can be used to assess different functional brain states [6][7][8][9]. However, a recorded EEG signal is highly contaminated with nonneuronal activities from different sources including eye blinking, eye movements, muscle movements, and electrocardiography (ECG) [10][11][12][13][14][15]. Eye movements and blinking generate high-magnitude artifacts as compared with the pure neuronal activity present in EEG data [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG‐Based Brain‐Computer Interface: A Comprehensive Study

et al. 2018

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It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were used with five different algorithms, namely, simple regression, least mean squares, recursive least squares, REGICA, and AIR. Results from statistical testing of time and frequency domain metrics suggested that a low-pass frequency between 6 and 8 Hz could be used as the most optimal filtering frequency of EOG signals, both to maximally overcome/minimize the effect of bidirectional contamination and to achieve good results from artifact removal algorithms. Furthermore, we also used BCI competition IV datasets to show the efficacy of the proposed framework on real EEG signals. The motor-imagery-based BCI achieved statistically significant high-classification accuracies when artifacts from EEG were removed by using 7 Hz low-pass filtering as compared to all other filterings of EOG signals. These results also validated our hypothesis that low-pass filtering should be applied to EOG signals for enhancing the performance of each algorithm before using them for artifact removal process. Moreover, the comparison results indicated that the hybrid algorithms outperformed the performance of single algorithms for both simulated and experimental EEG datasets.

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

Cited by 233 publications

References 27 publications

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

Top-down modulation of neural envelope tracking: the interplay with behavioral, self-report and neural measures of listening effort

Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG‐Based Brain‐Computer Interface: A Comprehensive Study

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