# Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network
## 3 Experiments
### Experimental Result  
Once the proposed HGRN model was trained, it could be tested by the validation database that its MEG decoding performance can be evaluated. For the public MEG decoding database of 16 subjects, the LOOCV experiments were carried out 16 times so that the corresponding results could be considered as the performance references of different MEG decoding methods.  
In Table 1, the results of the proposed HGRN model are presented. In addition, the experimental results of some state-of-the-art MEG decoding methods are also presented for comparison. To be clear, these methods are all supervised learning approaches which do not require any information of the testing data. Although some semi-supervised learning methods may achieve higher accuracy, they just utilized the information of the testing data which is not quite reasonable in practice [4]. In the method termed Pool [4; 22; 23], logistic regression with \(\ell 1\) penalization was used to model the MEG data of multiple subjects, and applied for decoding MEG signals of new subject. The Pool method did not consider the differences between different subjects that the corresponding inter-subject MEG decoding performance was not very well. By applying two logistic regression layers, the stacked generalization (SG) model achieved better performance than the Pool method [4; 24]. Considering the differences between subjects, stacked generalization with covariate shift (SG+CS) was proposed that different weighted parameters were applied for different subjects in the seconde logistic layer of SG+CS model [4; 35]. Moreover, the MEG decoding method based on Riemannian geometry achieved a good performance [15; 26]. To imitate deep structure of deep network, a method termed linear regression and random forest (LRRF) was proposed. The first layer of LRRF consisted of multiple logistic regression classifiers, and the output probabilities of the first layer was fed into a random forest for MEG decoding [25].  
As shown in Table 1, the proposed HGRN achieved higher performance than many other methods in the LOOCV experiments of 16 subjects. Specifically, the averaged MEG decoding accuracy of HGRN was 0.712 which was the highest in the table. The experimental results demonstrated that the proposed HGRN is an effective model for inter-subject MEG decoding.  
In order to compare the performance of the proposed method with the other methods, the Wilcoxon signed-rank test was applied for statistical analysis. As shown in Table 2, the Wilcoxon signed-rank test results between the proposed HGRN and the other methods are presented. Accordingly, the statistical analysis demonstrated that the HGRN achieved significantly better performance than the methods like Pool, SG, SG+CS and Gen. Although the proposed model did not achieve a significant better performance than LRRF, its averaged accuracy was a little higher than the LRRF as shown in Table 1. These results indicated the effectiveness of the proposed HGRN on MEG decoding performance.  
In the next section, we will discuss and analyze the internal properties of the proposed model. It may be significant for further research.