Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI

Mayeli, Ahmad; Henry, Kaylee; Wong, Chung Ki; White, Evan J.; Luo, Qing; Zotev, Vadim; Refai, Hazem H.; Bodurka, Jerzy

doi:10.1088/1741-2552/ac1037

Cited by 16 publications

(9 citation statements)

References 72 publications

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“…We found that, even though a clear reduction of the artifact was observed on the power spectra of our rs-EEG-fMRI data, none of the BCG artifact removal approaches tested (AAS, OBS, ICA, OBS-ICA, AAS-ICA, PROJIC-AAS, PROJIC-OBS) entirely preserved the spectral profile of EEG signals, due to both artifact residuals and induced EEG signal losses. Overall, in line with previous reports (Srivastava et al, 2005;Debener et al, 2006;Mayeli et al, 2021), we found better results with ICA-based approaches, especially when used after AAS or OBS, as compared to the conventional AAS and OBS and PROJIC approaches. Additionally, large variability in the artifact correction outcomes was observed, with some subjects even showing decreased absolute power compared to their outside rs-EEG, which may be reflecting EEG signal losses after the artifact correction procedure (Ullsperger and Debener, 2010;Marino et al, 2018).…”

Section: Discussionsupporting

confidence: 92%

“…Several signal processing tools have been developed to deal with the BCG artifact and reduce its contribution from the recordings while preserving EEG signal properties (Bullock et al, 2021;Ebrahimzadeh et al, 2022). As summarized in Bullock et al (2021) the most popular BCG correction approaches include Average Artifact Subtraction (AAS); (Allen et al, 1998), Optimal Basis Set (OBS); (Niazy et al, 2005), Independent Component Analysis (ICA); (Srivastava et al, 2005) and the combination of these: OBS-ICA (Debener et al, 2006) and AAS-ICA (Mayeli et al, 2021). Some other methods have also been recently proposed including the PROJIC-AAS and PROJIC-OBS methods (Abreu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Preservation of EEG spectral power features during simultaneous EEG-fMRI

et al. 2022

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IntroductionElectroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches.MethodElectroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods.ResultsThe BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis.DiscussionCurrent software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Preservation of EEG spectral power features during simultaneous EEG-fMRI

et al. 2022

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“…More specifically, MRI imaging artifacts within the EEG signal were reduced using the average artifact subtraction (AAS) method [54]. After downsampling EEG signals to 250 Hz, band-rejection filters with 1 Hz bandwidth [55][56][57] were applied to reduce fMRI slice selection fundamental frequency (19.5 Hz) and its harmonics, mechanical vibration noise (26 Hz) along with an alternating current power line noise (60 Hz). Another bandpass filter from 0.1 to 80 Hz (48 dB/octave) was applied to the EEG data.…”

Section: Eeg Data Preprocessingmentioning

confidence: 99%

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal

et al. 2021

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Objective. Electroencephalography microstates (EEG-ms), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. EEG-ms topographies are quasi-stable lasting between 60-120 milliseconds. Some evidence suggests that EEG-ms are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of EEG-ms and their association with functional MRI (fMRI) remains unclear. Approach. In a large cohort of healthy subjects (n = 52), we conducted several statistical and machine learning approaches analyses on the association among EEG-ms spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and machine learning approaches. Main results. Our results using a generalized linear model unraveled that EEG-ms transitions were largely and negatively associated with blood-oxygenation-level dependent (BOLD) signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between EEG-ms transitioning and fMRI signal while revealing that EEG-ms dynamics can predict BOLD signals and vice versa. Significance. Results suggest that EEG-ms transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities can measure common aspects of undergoing brain neuronal activities. Moreover, our results may help to better understand the electrophysiological interpretation of EEG-ms and solve several contradicting findings in the literature.

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“…Most prior studies used variations of AAS, which leaves substantial residual artifact. A few studies have also used a related independent component analysis-based method (Mayeli et al, 2021(Mayeli et al, , 2016. These existing online BCG artifact reduction techniques, while helpful, do not perform as well as reference-based methods (Hermans et al, 2016), and a better solution is required if real-time EEG-fMRI (rtEEG-fMRI) is to see more widespread adoption (Warbrick, 2022).…”

Section: Introductionmentioning

confidence: 99%

EEG-LLAMAS: an open source, low latency, EEG-fMRI neurofeedback platform

Levitt

Yang

Lewis

2022

Preprint

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Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source BCG removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.

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Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI

Cited by 16 publications

References 72 publications

Preservation of EEG spectral power features during simultaneous EEG-fMRI

Preservation of EEG spectral power features during simultaneous EEG-fMRI

Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal

EEG-LLAMAS: an open source, low latency, EEG-fMRI neurofeedback platform

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