applied sciences Article An Automatic Identification Method for the Blink Artifacts in the Magnetoencephalography with Machine Learning Yulong Feng 1,* , Wei Xiao 1, Teng Wu 1, Jianwei Zhang 2 , Jing Xiang 3 and Hong Guo 1,* 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;
[email protected] (W.X.);
[email protected] (T.W.) 2 School of Physics, Peking University, Beijing 100871, China;
[email protected] 3 MEG Centre, Division of Neurology, Cincinnati Children’s Hospital Medical Centre, Cincinnati, OH 45229, USA;
[email protected] * Correspondence:
[email protected] (Y.F.);
[email protected] (H.G.) Abstract: Magnetoencephalography (MEG) detects very weak magnetic fields originating from the neurons so as to study human brain functions. The original detected MEG data always include interference generated by blinks, which can be called blink artifacts. Blink artifacts could cover the MEG signal we are interested in, and therefore need to be removed. Commonly used artifact cleaning algorithms are signal space projection (SSP) and independent component analysis (ICA). These algorithms need to locate the blink artifacts, which is typically done with the identification of the blink signals in the electrooculogram (EOG). The EOG needs to be measured by electrodes placed near the eye. In this work, a new algorithm is proposed for automatic and on-the-fly identification of the blink artifacts from the original detected MEG data based on machine learning; specifically, the artificial neural network (ANN). Seven hundred and one blink artifacts contained in eight MEG signal Citation: Feng, Y.; Xiao, W.; Wu, T.; data sets are harnessed to verify the effect of the proposed blink artifacts identification algorithm.