## 4 Results and Discussion
### Performance Analysis for All Subjects
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Tanh** \\ \hline Subjects 1 & 75.86 & 77.59 & 81.03 & 77.59 \\ Subjects 2 & 55.17 & 63.79 & 65.52 & 63.79 \\ Subjects 3 & 87.93 & 86.21 & 87.93 & 84.48 \\ Subjects 4 & 46.55 & 55.17 & 50.00 & 43.10 \\ Subjects 5 & 50.00 & 46.55 & 48.28 & 41.38 \\ Subjects 6 & 36.21 & 22.41 & 31.03 & 34.48 \\ Subjects 7 & 63.79 & 65.52 & 75.86 & 70.69 \\ Subjects 8 & 75.86 & 74.14 & 77.59 & 74.14 \\ Subjects 9 & 63.79 & 65.52 & 58.62 & 72.41 \\
**Mean value** & **61.68** & **61.87** & **64.00** & **62.45** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification accuracy of the proposed modified merged CNNs methods (%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline Subjects 1 & 56.90 & 65.52 & 34.48 & 63.79 \\ Subjects 2 & 43.10 & 43.10 & 41.38 & 50.00 \\ Subjects 3 & 36.21 & 60.34 & 63.79 & 53.45 \\ Subjects 4 & 36.21 & 36.21 & 27.59 & 29.31 \\ Subjects 5 & 32.76 & 43.10 & 37.93 & 29.31 \\ Subjects 6 & 20.69 & 20.69 & 20.69 & 37.93 \\ Subjects 7 & 29.31 & 58.62 & 67.24 & 70.69 \\ Subjects 8 & 15.52 & 36.21 & 55.17 & 55.17 \\ Subjects 9 & 36.21 & 50.00 & 50.00 & 41.38 \\
**Mean value** & **34.10** & **45.97** & **44.25** & **47.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracy of the proposed merged CNNs methods (%). In [33], the authors used the Latent Dirichlet Allocation (LDA) to classify Motor Imagery. They achieved an accuracy value of 63.81%. In reference [34], the authors compared their proposed PSO-based Fuzzy Logic System (FLS) with many competing approaches, including Naive Bayes (NB), AdaBoostM2 ensemble (Ensemble), and Support Vector Machines (SVM). They applied these machine learning methods using the functions of Matlab, namely, respectively; fitch, fitsensemble, and fitecec. For the SVM method, they used the one-vs-all encoding scheme with a binary SVM learner using the Gaussian kernel. And for the Ensemble method, the authors selected AdaBoostM2 as the classification algorithm, and the decision tree as the learner, with the ensemble learning cycle number set to 100. Our proposed method based on CNN1 gave the best accuracy value that may be due to the simple preprocessing we applied to the dataset. In addition, the extraction of frequency characteristics using WPD and spatial characteristics using CSP, improved the rate of classification accuracy of MI. First, our experimental results confirmed the feasibility of the approach based on the CNN1 in the EEG domain, then the effectiveness of the proposed method compared to the methods of the state of the art. We see that the resulting networks successfully learn important characteristics of MI-EEG signals, thus improving overall performance. However, there are still several difficult issues to resolve:  
* In this work, merging the two CNNs and adding the LSTM layers did not improve the classification result. These two points can work in this direction either by adding other temporal characteristics, for example, or by applying other fusion methods other than concatenation and the merging by the perceptron monolayer. * According to Table 7, the methods of the state of the art sometimes surpass our proposed method based on the CNN1 models for a few subjects. It is expected that various architectural extensions, such as adding more convolutional layers, can be applied to our proposed method based on CNN1; however, the effects of these extensions for the EEG domain are not clear. Therefore, we will attempt to extend the architecture of the merged CNNs to determine if the architectural change can affect the overall performance of the system. As discussed in this section, the use of methods based on the fusion of CNNs can be improved in several directions. We will discuss the aforementioned issues to improve the performance of MI-EEG classification.