## 4 Results and Discussion
### Performance Analysis for All Subjects  
Tables 2-6 give the accuracy values obtained by the application of our proposed methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline Subjects 1 & 65.52 & 67.24 & 0.63.79 & 67.24 \\ Subjects 2 & 41.38 & 46.55 & 0.56.90 & 50.00 \\ Subjects 3 & 74.14 & 68.97 & 0.72.41 & 77.59 \\ Subjects 4 & 32.76 & 39.66 & 39.66 & 43.10 \\ Subjects 5 & 41.38 & 37.93 & 44.83 & 43.10 \\ Subjects 6 & 36.21 & 29.31 & 32.76 & 34.48 \\ Subjects 7 & 75.86 & 72.41 & 63.79 & 75.86 \\ Subjects 8 & 51.72 & 50.00 & 48.28 & 50.00 \\ Subjects 9 & 50.00 & 50.00 & 55.17 & 48.28 \\
**Mean value** & **52.10** & **55.55** & **53.08** & **54.40** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy of the proposed CNN2 methods (%). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **Tanh** \\ \hline Subjects 1 & 72.41 & 60.34 & 58.62 & 63.79 \\ Subjects 2 & 60.34 & 58.62 & 53.45 & 58.62 \\ Subjects 3 & 75.86 & 77.59 & 84.48 & 82.76 \\ Subjects 4 & 29.31 & 31.03 & 31.03 & 31.03 \\ Subjects 5 & 51.72 & 53.45 & 55.17 & 58.62 \\ Subjects 6 & 48.28 & 46.55 & 44.83 & 27.59 \\ Subjects 7 & 74.14 & 70.69 & 62.07 & 65.52 \\ Subjects 8 & 60.34 & 67.24 & 62.07 & 60.34 \\ Subjects 9 & 44.83 & 41.38 & 46.55 & 44.83 \\
**Mean value** & **57.47** & **56.32** & **55.36** & **54.78** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of the proposed ANN methods (%). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline Subjects 1 & 70.69 & 79.31 & 81.03 & 68.97 \\ Subjects 2 & 58.62 & 67.24 & 56.90 & 68.97 \\ Subjects 3 & 89.66 & 89.66 & 81.03 & 86.21 \\ Subjects 4 & 50.00 & 44.83 & 53.45 & 53.45 \\ Subjects 5 & 58.62 & 53.45 & 55.17 & 43.10 \\ Subjects 6 & 37.93 & 29.31 & 31.03 & 36.21 \\ Subjects 7 & 62.07 & 77.59 & 70.69 & 62.07 \\ Subjects 8 & 65.52 & 74.14 & 74.14 & 75.86 \\ Subjects 9 & 39.66 & 46.55 & 53.45 & 48.28 \\
**Mean value** & **59.19** & **62.45** & **61.87** & **68.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy of the proposed CNN1 methods (%). From these tables, we can conclude that the proposed CNN1 method achieves the best average classification accuracy of 68.77% with the Tanh function. On the other hand, the comparison between the results obtained by the proposed models CNN1 and CNN2 shows that the addition of the LSTM layers in the CNN model does not improve this model. In addition, merging the two CNNs without adding LSTM layers gives acceptable results. According to Tables 5 and 6, removing the Max Pooling steps, LSTM layers, and the final SoftMax classification layer from CNN1 and CNN2, and concatenating the features using a perceptron monolayer, improve the results of motor imagery tasks classification. The modified merged CNNs give an improvement in accuracy but the accuracy achieved by CNN1 was the best overall despite being simple. We can also conclude that based on the comparison of the subject-specific precision obtained by our proposed models, each model gave better results for a different subject. A comparison of the classification accuracies of the proposed method based on CNN1 and other state-of-the-art methods is presented in Table 7.  
As shown in Table 7, our proposed CNN1 method is better than all other machine learning methods with an average classification accuracy of 68.77%, while the NB method is the worst with an average classification accuracy of 58.20%. On the other hand, the average classification accuracy of the FBCSP is 67.21%, which is indicative of the result of the BCI competition. FBCSP [32] applied to the BCI competition achieved acceptable results with the handcrafted characteristics for each subject. However, it is impossible to find such an optimal craft characteristic for each new subject; therefore, all methods were evaluated under the same conditions in our experiments.