## 4 Discussion  
In this work, blink artifacts identification is achieved by the ANN. Eight data sets collected from four different subjects are used to verify the recognition effect of the ANN. The first, third, fifth, and seventh data sets are employed to train the ANN. All eight data sets are used to test the ANN. The accuracy of the identification results shows the method can recognize the blink artifacts in the MEG signal. Nevertheless, the false alarms and the missing alarms appear. The missing alarms mainly result from insufficient training data. The MEG signals are signals with high inter-individual variability. Different subjects may produce different MEG signals. If we used the MEG data from one subject to train the network, and then used the trained network to identify the blink artifacts measured from another subject, the accuracy would drop dramatically. Considering that, using the ANN to recognize the blink artifacts generated by different subjects still needs further research. In addition, due to the different states of the subject during the experiment and the different measuring environment, the blink signals produced by the same subject may vary. The false alarms may be bound to the characteristics of the MEG signals and not to the characteristics of the blink artifacts. Some MEG signals may exhibit similar characteristics to the blink artifacts, resulting in misjudgment by the ANN. If various training data sets could be used to train the network, the recognition accuracy might be further improved, which needs to be proved by further experiments.  
When recognizing spatial distribution characteristics of blink artifacts using the ANN, the identification algorithm does not introduce additional electrodes, and it can be implemented automatically and simultaneously with data measurement. These advantages endow the method with the ability, when combined with the SSP and the ICA algorithm, to realize the automatic and on-the-fly rejection for blink artifacts. Nevertheless, volumes of calibrated data are required to train the ANN to improve its accuracy. In this work, 66,780 2D images generated from eight MEG signal data sets are used for training and testing GoogLeNet. If we want to apply this method to practical problems, various types of artifacts are needed to train the ANN, which are missing in this experiment and need to be further supplemented.  
In addition, if other artifacts data, such as eye movement artifacts and cardiac artifacts, are collected and employed for feature extraction, the ANN has the potential to classify and identify these artifacts in the MEG. The eye movement and cardiac artifacts are regarded as noise in the MEG and need to be removed, but for some other typical applications, such as the research on relationships between the eye movements and human brain activities, our method provides thoughts to extract the information from these artifacts. In addition, the machine learning algorithm has the potential to be migrated and applied to signal source estimation in the future. In magnetic source imaging (MSI), a more suitable network could be employed to classify the signals emitted from different parts of the brain, to establish the map between the location of the signal sources and the detected signal. Furthermore, the ANN should be capable of making a preliminary diagnosis by identifying the characteristics of the MSI. If such work can be realized through machine learning, it would hopefully reduce the burden of doctors and improve the efficiency of diagnosis and treatment.