MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.
MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.
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DOI：
10.1016/j.neuroimage.2021.118402
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发表时间：
2021-11-01
期刊：
影响因子：
5.7
通讯作者：
Montillo, Albert A.
中科院分区：
文献类型：
--
作者：
Treacher, Alex H.;Garg, Prabhat;Davenport, Elizabeth;Godwin, Ryan;Proskovec, Amy;Bezerra, Leonardo Guimaraes;Murugesan, Gowtham;Wagner, Ben;Whitlow, Christopher T.;Stitzel, Joel D.;Maldjian, Joseph A.;Montillo, Albert A.
Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the most common sources of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG), and chest electrodes, as in electrocardiography (ECG), however this complicates imaging setup, decreases patient comfort, and can induce further artifacts from movement. This work proposes an EOG- and ECG-free approach to identify eye-blinks, saccades, and cardiac activity signals for automated artifact suppression. The contribution of this work is three-fold. First, using a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA), a highly accurate artifact classifier is constructed as an amalgam of deep 1-D and 2-D Convolutional Neural Networks (CNNs) to automate the identification and removal of ubiquitous whole brain artifacts including eye-blink, saccade, and cardiac artifacts. The specific architecture of this network is optimized through an unbiased, computer-based hyperparameter random search. Second, visualization methods are applied to the learned abstraction to reveal what features the model uses and to bolster user confidence in the model’s training and potential for generalization. Finally, the model is trained and tested on both resting-state and task MEG data from 217 subjects, and achieves a new state-of-the-art in artifact detection accuracy of 98.95% including 96.74% sensitivity and 99.34% specificity on the held out test-set. This work automates MEG processing for both clinical and research use, adapts to the acquired acquisition time, and can obviate the need for EOG or ECG electrodes for artifact detection.
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影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
DOI：
10.1109/prni.2017.7981506
发表时间：
2017-06-01
期刊：
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging
影响因子：
--
作者：
Garg, Prabhat;Davenport, Elizabeth;Montillo, Albert
通讯作者：
Montillo, Albert
影响因子：
5.7
作者：
Fatima, Zainab;Quraan, Maher A.;McIntosh, Anthony Randal
通讯作者：
McIntosh, Anthony Randal