Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network

As an effective brain signal recording technique for neuroscience, magnetoencephalography (MEG) is widely used in cognitive research. However, due to the low signal-to-noise ratio and the structural or functional variabilities of MEG signals between different subjects, conventional methods perform poorly in decoding human brain responds. Inspired by deep recurrent network for processing sequential data, we applied the gated recurrent units for MEG signals processing. In the paper, we proposed a hybrid gated recurrent network (HGRN) for inter-subject visual MEG decoding. Without the need of any information from test subjects, the HGRN effectively distinguished MEG signals evoked by different visual stimulations, face and scrambled face. In the leave-one-out cross-validation experiments on sixteen subjects, our method achieved better performance than many existing methods. For more in-depth analysis, HGRN can be utilized to extract spatial features and temporal features of MEG signals. These features conformed to the previous cognitive studies which demonstrated the practicality of our method for MEG signal processing. Consequently, the proposed model can be considered as a new tool for decoding and analyzing brain MEG signal, which is significant for visual cognitive research in neuroscience.