An Automatic Identification Method for the Blink Artifacts in the Magnetoencephalography with Machine Learning  
[PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks,  
Department of Electronics, and Centre for Quantum Information Technology, Peking University,  
Beijing 100871, China; xiao, [EMAIL_ADDRESS] (W.X. ); [EMAIL_ADDRESS] (T.W)  
[PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks,  
Department of Electronics, and Centre for Quantum Information Technology, Peking University,  
Beijing 100871, China; xiao, [EMAIL_ADDRESS] (W.X. ); [EMAIL_ADDRESS] (T.W)  
[PERSON]  
1 State Key Laboratory of Advanced Optical Communication Systems and Networks,  
Department of Electronics, and Centre for Quantum Information Technology, Peking University,  
Beijing 100871, China; xiao, [EMAIL_ADDRESS] (W.X. ); [EMAIL_ADDRESS] (T.W)  
[PERSON]  
2 School of Physics, Peking University, Beijing 100871, China; [EMAIL_ADDRESS] (M.E.Centre, Division of Neurology, Cincinnati Children's Hospital Medical Centre,  
Cincinnati, OH 45229, USA; [EMAIL_ADDRESS] (Correspondence: [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (H.G.) 2021 11 2415 222  
projection (SSP) [25; 26; 27; 28] and independent component analysis (ICA) [29; 30] are widely used to remove the blinks and other artifacts in the MEG. However, these two methods may need extra electrodes to measure the blink signals in the electrooculogram (EOG) and decide the occurrence time of the blink artifacts in the MEG for practical use. A professional staff is also needed to identify the blink artifacts. To make up for the shortcomings of these two algorithms, some decent automatic processes for artifacts rejection based on statistics have been proposed by many research groups [31; 32; 33; 34; 35]. Machine learning algorithms are also introduced into the MEG data processing [36; 37]. Some algorithms [38; 39] have been proposed to classify different artifacts in the MEG and EEG (electroencephalography) based on machine learning. Most of these algorithms require data preprocessing, such as ICA, to extract the feature vectors of the artifacts. The preprocessing essentially reduces the dimension of the original detected MEG signal and then recombines these signals. The dimension reduction distorts the spatial dimension of the detected signal, which not only results in the loss of information of the original signal, but also adds some redundant messages into the MEG signal segments that are not influenced by the blink artifacts. In this work, we use the artificial neural network (ANN) to analyze the blink artifacts from the original detected MEG signal, and make full use of the information of their spatial distribution features, so as to realize its automatic identification. With the development of computer science and information theory, the ANN is proposed as a powerful computing tool [40; 41; 42]. The ANN is a model built from the human brain neural network and can form different structures according to different connection methods, aiming to solve classification problems. It is a persuasive tool to extract data characteristics and eliminate noise, which plays a major role in various fields, such as automatic speech recognition (ASR) [43; 44], computer image processing [45], and biomedical imaging [46; 47]. The objective of the present study is to identify blink artifacts from the original detected MEG data. To address the aim, the original detected MEG data are color-coded into 2D images to display the spatial distribution of the blink artifacts, which are characteristic. These 2D images are taken as the input of the GoogLeNet [48] to automatically identify the blink artifacts. GoogLeNet is a convolutional neural network (CNN), which is a typical ANN and can recognize image features, indicating that no extra electrodes and professional confirmation are needed for artifacts identification and providing a novel perspective for the analysis of artifacts in the MEG.