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Levitt J, Yang Z, Williams SD, Lütschg Espinosa SE, Garcia-Casal A, Lewis LD. EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI. Neuroimage 2023; 273:120092. [PMID: 37028736 PMCID: PMC10202030 DOI: 10.1016/j.neuroimage.2023.120092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/02/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
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 artifact 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 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. 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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Affiliation(s)
- Joshua Levitt
- Department of Biomedical Engineering, Boston University, USA
| | - Zinong Yang
- Department of Biomedical Engineering, Boston University, USA; Graduate Program of Neuroscience, Boston University, USA
| | | | | | | | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, USA; Institute for Medical Engineering and Sciences, Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA.
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Kraljič A, Matkovič A, Purg N, Demšar J, Repovš G. Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI. FRONTIERS IN NEUROIMAGING 2022; 1:968363. [PMID: 37555133 PMCID: PMC10406266 DOI: 10.3389/fnimg.2022.968363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/02/2022] [Indexed: 08/10/2023]
Abstract
Multimodal neuroimaging using EEG and fMRI provides deeper insights into brain function by improving the spatial and temporal resolution of the acquired data. However, simultaneous EEG-fMRI inevitably compromises the quality of the EEG and fMRI signals due to the high degree of interaction between the two systems. Fluctuations in the magnetic flux flowing through the participant and the EEG system, whether due to movement within the magnetic field of the scanner or to changes in magnetic field strength, induce electrical potentials in the EEG recordings that mask the much weaker electrical activity of the neuronal populations. A number of different methods have been proposed to reduce MR artifacts. We present an overview of the most commonly used methods and an evaluation of the methods using three sets of diverse EEG data. We limited the evaluation to open-access and easy-to-use methods and a reference signal regression method using a set of six carbon-wire loops (CWL), which allowed evaluation of their added value. The evaluation was performed by comparing EEG signals recorded outside the MRI scanner with artifact-corrected EEG signals recorded simultaneously with fMRI. To quantify and evaluate the quality of artifact reduction methods in terms of the spectral content of the signal, we analyzed changes in oscillatory activity during a resting-state and a finger tapping motor task. The quality of artifact reduction in the time domain was assessed using data collected during a visual stimulation task. In the study we utilized hierarchical Bayesian probabilistic modeling for statistical inference and observed significant differences between the evaluated methods in the success of artifact reduction and associated signal quality in both the frequency and time domains. In particular, the CWL system proved superior to the other methods evaluated in improving spectral contrast in the alpha and beta bands and in recovering visual evoked responses. Based on the results of the evaluation study, we proposed guidelines for selecting the optimal method for MR artifact reduction.
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Affiliation(s)
- Aleksij Kraljič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Nina Purg
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Jure Demšar
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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3
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Rekavandi AM, Seghouane AK, Evans RJ. Adaptive Brain Activity Detection in Structured Interference and Partially Homogeneous Locally Correlated Disturbance. IEEE Trans Biomed Eng 2022; 69:3064-3073. [PMID: 35320080 DOI: 10.1109/tbme.2022.3161292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data where the noise is possibly locally correlated, using the sample covariance estimator is not a suitable choice due to significant dependency of its accuracy on the number of observations. METHODS In this study, we take advantage of an assumed banded structure in the covariance matrix to model the local dependence in the noise and propose a new covariance estimation approach. In particular, we use the idea of fac-torizing the joint likelihood function into a few conditional likelihood terms and maximizing each term independently of the others. This process leads to an explicit estimator for banded covariance matrices which requires fewer observations to achieve the same accuracy as the sample covari-ance. This estimate is then fed into an adaptive matched filter, two-step Rao and two-step Wald tests for detection. RESULTS Simulation results reveal the superiority of the proposed methods over well known classical detectors. Finally, the proposed methods are applied to functional magnetic resonance imaging (fMRI) data to localize neural activities in the brain. CONCLUSION The proposed method can offer better activation maps in terms of accuracy and spatial smoothness. SIGNIFICANCE The proposed methods can be seen as alternatives for standard detection approaches which are not perfectly aligned with the properties of fMRI data.
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Cruttenden CE, Taylor JM, Ahmadi M, Zhang Y, Zhu XH, Chen W, Rajamani R. Reference-Free Adaptive Filtering of Extracellular Neural Signals Recording in Ultra-High Field Magnetic Resonance Imaging Scanners: Removal of Periodic Interferences. Biomed Signal Process Control 2022; 71:102758. [PMID: 35069775 PMCID: PMC8782249 DOI: 10.1016/j.bspc.2021.102758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
This paper focuses on the removal of periodic artifacts from neural signals recorded in rats in ultra-high field (UHF) MRI scanners, using a reference free adaptive feedforward method. Recording extracellular neural signals in the UHF environment is motivated by the desire to combine neural recording and UHF functional magnetic resonance imaging (fMRI) to better understand brain function. However, the neural signals are found to have extremely high noise artifacts of a periodic nature due to electromagnetic interference and due to small oscillatory motions. In particular, noise at 60 Hz and several harmonics of 60 Hz, sinusoidal noise from a pump, and low frequency breathing motion artifacts are observed. Due to significant overlap between the noise frequencies and the neural frequency region of interest, band pass filters cannot be effectively utilized in this application. Hence, this paper develops adaptive least squares feedforward cancellation filters to remove the periodic artifacts. The interference fundamental frequency is identified precisely using an implementation of k-means in an iterative approach. The paper includes significant animal data from rats recorded in an IACUC-approved procedure in 9.4T and 16.4T MRI machines. For breathing artifacts filtered from 4 rats, the mean signal cancellation values at the harmonic interference frequencies are 5.18, 12.97, and 20.87 dB/Hz for a sliding template subtraction, a single-stage impulse reference method, and the cascaded adaptive filtering approach respectively. For pump artifacts filtered from 2 chronically implanted rats, mean signal cancellation values are 2.85, 9.52 and 12.06 dB/Hz respectively. The experimental results show that periodic noise is very effectively removed by the developed cascaded adaptive least squares feedforward algorithm.
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Affiliation(s)
- Corey E. Cruttenden
- University of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455.,University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Jennifer M. Taylor
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455.,University of Minnesota Department of Biomedical Engineering, 312 Church St. SE, Minneapolis MN 55455
| | - Mahdi Ahmadi
- University of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455
| | - Yi Zhang
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Xiao-Hong Zhu
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Wei Chen
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Rajesh Rajamani
- University of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455.,Correspondence
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Bullock M, Jackson GD, Abbott DF. Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage. Front Neurol 2021; 12:622719. [PMID: 33776886 PMCID: PMC7991907 DOI: 10.3389/fneur.2021.622719] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 01/29/2021] [Indexed: 11/13/2022] Open
Abstract
Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15–20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.
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Affiliation(s)
- Madeleine Bullock
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Graeme D Jackson
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
| | - David F Abbott
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
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Prasad DS, Chanamallu SR, Prasad KS. Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform. Comput Methods Biomech Biomed Engin 2020; 24:551-578. [PMID: 33245687 DOI: 10.1080/10255842.2020.1839893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous efforts have been taken to improve its quality by eliminating the artifacts. The EEG involves the biological artifacts (ocular artifacts, ECG and EMG artifacts), and technical artifacts (noise from the electric power source, amplitude artifacts, etc.). From these physiological artifacts, ocular activities are one of the most well-known over other noise sources. Reducing the risks of this event and avoid it is practically very difficult, even impossible, as the ocular activities are involuntary tasks. To trim down the effect of ocular artifacts overlapping with EEG signal and overwhelm the subjected flaws, few intelligent approaches have to be developed. This proposal tempts to implement a novel method for detecting and preventing ocular artifacts from the EEG signal. The developed model involves two main phases: (a) Detection of Ocular artifacts and (b) Removal of ocular artifacts. For detecting the ocular artifacts, initially, the EEG is subjected to decomposition process using 5-level Discrete Wavelet Transform (DWT), and Empirical Mean Curve Decomposition (EMCD). Next to the decomposition process, the features like kurtosis, variance, Shannon's entropy, and few first-order statistical features are extracted. These features will be helpful for the detection process in the classification side. For detecting the ocular artifacts from the decomposed signal, the extracted features are subjected to a machine learning algorithm called Neural Network (NN). As an improvement to the conventional NN, the training algorithm of ANN is improved by the improved Earth Worm optimization Algorithm (EWA) termed as Dual Positioned Elitism-based EWA (DPE-EWA), which updates the weight of NN to improve the performance. In the Removal phase, the optimized Lifting Wavelet Transform (LWT) is deployed, in which the improvement is made on optimizing the filter coefficients using the proposed DPE-EWA. Thus, the integration of optimized NN and optimized LWT suggests a potential possibility to accommodate the detection and removal of ocular artifacts that exist in the EEG signals.
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Su J, Yang Z, Yan W, Sun W. Electroencephalogram classification in motor-imagery brain-computer interface applications based on double-constraint nonnegative matrix factorization. Physiol Meas 2020; 41:075007. [PMID: 32590360 DOI: 10.1088/1361-6579/aba07b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) are aimed at providing a new way of communication between the human brain and external devices. One of the major tasks associated with the BCI system is to improve classification performance of the motor imagery (MI) signal. Electroencephalogram (EEG) signals are widely used for the MI BCI system. The raw EEG signals are usually non-stationary time series with weak class properties, degrading the classification performance. APPROACH Nonnegative matrix factorization (NMF) has been successfully applied to pattern extraction which provides meaningful data presentation. However, NMF is unsupervised and cannot make use of the label information. Based on the label information of MI EEG data, we propose a novel method, called double-constrained nonnegative matrix factorization (DCNMF), to improve the classification performance of NMF on MI BCI. The proposed method constructs a couple of label matrices as the constraints on the NMF procedure to make the EEGs with the same class labels have the similar representation in the low-dimensional space, while the EEGs with different class labels have dissimilar representations as much as possible. Accordingly, the extracted features obtain obvious class properties, which are optimal to the classification of MI EEG. MAIN RESULTS This study is conducted on the BCI competition III datasets (I and IVa). The proposed method helps to achieve a higher average accuracy across two datasets (79.00% for dataset I, 77.78% for dataset IVa); its performance is about 10% better than the existing studies in the literature. SIGNIFICANCE Our study provides a novel solution for MI BCI analysis from the perspective of label constraint; it provides convenience for semi-supervised learning of features and significantly improves the classification performance.
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Affiliation(s)
- Jing Su
- School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou 510006, People's Republic of China
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Hosseini MP, Tran TX, Pompili D, Elisevich K, Soltanian-Zadeh H. Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing. Artif Intell Med 2020; 104:101813. [DOI: 10.1016/j.artmed.2020.101813] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 12/26/2019] [Accepted: 01/31/2020] [Indexed: 11/28/2022]
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9
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Anem J, Kumar GS, Madhu R. Cat Swarm Fractional Calculus optimization-based deep learning for artifact removal from EEG signal. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1704438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Jayalaxmi Anem
- ECE, Aditya Institute of Technology and Management, Tekkali, India
| | - G. Sateesh Kumar
- ECE, Aditya Institute of Technology and Management, Tekkali, India
| | - R. Madhu
- ECE, Jawaharlal Nehru Technological University, Kakinada, India
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10
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Zhang S, Hennig J, LeVan P. Direct modelling of gradient artifacts for EEG-fMRI denoising and motion tracking. J Neural Eng 2019; 16:056010. [PMID: 31216524 DOI: 10.1088/1741-2552/ab2b21] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Simultaneous electroencephalography and functional magnetic resonance imaging recording (EEG-fMRI) has been widely used in neuroscientific and clinical research. The artifacts in the recorded EEG resulting from rapidly switching magnetic field gradients are usually corrected by average-artifact subtraction (AAS) due to their repetitive nature. But the performance of AAS is often disrupted by altered artifact waveforms across epochs, notably due to head motion. APPROACH Here, a method is proposed to make use of the known MR sequence gradient waveforms for a direct modelling of gradient artifacts. After accounting for filtering effects on the gradient artifacts, a continuous modulation of the gradient waveforms superimposed on the EEG signal is obtained. MAIN RESULTS Although a moving AAS template can adjust to slow drifts in gradient artifact variation, it fails to adapt to abrupt motion, resulting in residual noise. We demonstrate how this modelling approach can reduce motion-affected gradient artifacts without distorting the underlying neuronal signals. Moreover, the method provides useful head motion information highly correlated with motion tracked by an optical camera. SIGNIFICANCE Our work provides a novel way to improve gradient artifact removal in EEG-fMRI, and shows a potential to detect head motion without requiring additional hardware.
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Affiliation(s)
- Shuoyue Zhang
- Department of Radiology - Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Daniel AJ, Smith JA, Spencer GS, Jorge J, Bowtell R, Mullinger KJ. Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI. Hum Brain Mapp 2018; 40:578-596. [PMID: 30339731 PMCID: PMC6492138 DOI: 10.1002/hbm.24396] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/30/2018] [Accepted: 08/31/2018] [Indexed: 11/25/2022] Open
Abstract
Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.
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Affiliation(s)
- Alexander J Daniel
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - James A Smith
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Glyn S Spencer
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.,Department of Physics, Loughborough University, Leicestershire, United Kingdom
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.,Birmingham University Imaging Centre, School of Psychology, University of Birmingham, Birmingham, United Kingdom
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Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: Implications for motion artefact correction. Neuroimage 2018; 173:188-198. [PMID: 29486322 PMCID: PMC5929889 DOI: 10.1016/j.neuroimage.2018.02.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 02/16/2018] [Indexed: 11/24/2022] Open
Abstract
Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject's head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity. Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals. Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data.
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