# Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network
###### Abstract  
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. inter-subject; brain signal decoding; spatial/temporal features +
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The experimental procedure of this dataset is shown in Figure 1. During the experiments, the subject was asked to look at pictures with face image or scrambled face image. In the meanwhile, a MEG recording system will record his/her brain magnetic signals. As shown in Figure 1, we can find that the MEG signals in different scalp locations had different signal components. Based on the recorded MEG signals and the corresponding visual stimulations, a decoding model can be built to predict the visual information (face or scrambled face image) from MEG signals. Consequently, different MEG decoding methods can be trained and evaluated by a common dataset [4,8]. According to previous research, conventional methods usually rely on ensemble averaging technique to deal with the low signal-to-noise ratio of brain signals and acquire reliable detection [9]. However, the ensemble averaging technique might erase some important information in each trial of ERP signals [10]. According to some neuroscience researches, continuous gamma effect was found in the averaged brain signals in multiple trials [11,12]. It means that studying single-trial signals is essential for understanding the dynamic change of human brain. Moreover, decoding brain signals in an inter-subject manner is significant in neuroscience researches [4,13,14]. For example, a transductive transfer learning (TTL) algorithm was proposed for inter-subject MEG decoding with single-trial samples [4]. To extract manifold features of MEG signals, a new MEG decoding method based on riemannian metric was applied, which achieved a high decoding accuracy [15]. Moreover, as a popular machine learning approach in many areas, random forest was applied for MEG decoding [16]. The random forest algorithm can effectively extract MEG features of different subjects and improve inter-subject MEG decoding performance. In the past few years, deep learning methodology was widely used and achieved remarkable results in many areas. As for brain signal processing, deep learning methods also worked well in extracting features, detecting abnormal components, classifications and so on. For example, deep network based on restricted Boltzmann machine (RBM) achieved good performance in processing electroencephalogram(EEG) signals [17,18]. Convolutional neural network (CNN) was firstly applied for detecting event-related potentials from EEG signals [19]. Moreover, deep learning method was utilized to study the dynamics of scene representations in the human brain revealed by MEG [20].