# Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network
## 4 Discussion  
Based on GRU structure, the proposed HGRN model was built to capture the features of MEG signals and decode the signals. In this section, the proposed HGRN model will be discussed in details. First of all, the performance of the proposed model to decode face image stimulation and scrambled face image stimulation were presented in confusion matrix. Based on confusion matrix, the decoding performance difference between the two stimulations can be analyzed. Secondly, the weights of MEG sensors on the scalp for decoding signals are extracted. Consequently, the spatial distribution of the MEG signals  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Pool** & **SG** & **SG+CS** & **Gen** & **LRRF** & **HGRN** \\
**Sub. ** & [22] & [36] & [4] & [15] & [25] & **Ours** \\ \hline
1 & 0.62 & 0.67 & 0.71 & 0.755 & 0.764 & 0.759 \\
2 & 0.64 & 0.63 & 0.65 & 0.675  
in the decoding experiments can be determined. Lastly, the MEG signals evoked by two different stimulations will be extracted for analysis. To analyze the MEG decoding performance of HGRN, confusion matrix is applied and presented in Figure 4. Specifically, the MEG signals evoked by face images were considered as positive class, while the MEG signals evoked by scrambled face images were categorized as the negative class. In the experiment, the true positive category indicated that the predicted results and the ground truth are all positive class. Likewise, the true negative category indicated that the predicted results and the ground truth are the same negative class. The false positive indicated that the predicted results are positive class while the ground truth is negative. The false negative indicated that the predicted results are negative class while the ground truth is positive. The TP, TN, FP and FN denote the number of samples in the categories of true positive, true negative, false positive and false negative, respectively. Then, the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR) are calculated. These results were calculated with all 16 subjects' results in the LOOCV experiments. As shown in Figure 4, the number of samples in the categories of true positive, true negative, false positive and false negative are presented in the confusion matrix. Moreover, the corresponding TPR, TNR, FPR and FNR are also presented. According to Figure 4, the TPR and TNR were closed to 71%. The corresponding recall, precision and F1 scores are 71%, 72% and 71%, respectively. These results demonstrated that the HGRN model can evenly distinguish the positive samples and negative samples (i.e., MEG signals evoked by face images and scrambled face images). The accuracy is a proper metric to evaluate balance datasets according to the previous study [37]. In the used dataset, the number of the positive and negative samples are 4693 and 4721 that it can be considered as a balance dataset. The accuracies presented in Table 1 can be used for the comparison between the performance of the other models and ours. In addition, about 30 percent of samples were incorrectly categorized. That is to say, there was still a lot of room for improving MEG decoding performance. In order to study the spatial features of MEG signals, we applied a leave-one-alone strategy that only one MEG channel was reserved for analysis while the rest channels were clamped to zero. Next, the new MEG signals were tested by the HGRN model which is trained previously. Then, the MEG decoding result corresponding to each reserved channel can be obtained. To a certain extent, these results can be considered as the contributions of MEG channels in the decoding experiments. By normalizing these results to [0, 1], the weight of each channel for MEG decoding is calculated. Then, the spatial feature of each subject can be obtained. As shown in Figure 5, the spatial feature of MEG signals of Subject 1 is presented that its top view and side view are presented in the left subfigure and right subfigure, respectively.