## 4 Experiments and Results
### EEG-TCNet with Different Hyperparameters  
The detailed architecture of EEG-TCNet is described in Table 1. C and \(T\) denote the number of EEG channels and the number of time points. In the first sub-project, \(C=22\) and \(T=1000\) because the time points used in training are 4 s from the cue section to the end of the motor imagery section. The baseline hyperparameters of EEG-TCNet are \(F_{1}=8,K_{E}=32,F_{T}=12,K_{T}=4\), the same as those for the authors. Besides that, the dropout values in the EEGNet module and the TCN module are \(\rho_{e}=0.2\), \(\rho_{t}=0.3\), the same as in the original paper. Where \(\rho_{e}\) is dropout in EEGNet module and \(\rho_{t}\) is dropout in TCN module. In the first sub-project, EEG-TCNet is trained with different numbers of filters and kernel sizes in the EEGNet module and TCN module, as described in Table 2.  
In Table 3, the contribution of each \(F_{1},K_{E},F_{T},K_{T}\) to the performance of EEG-TCNet is examined by keeping the baseline value of three parameters and changing one parameter. The average accuracy of EEG-TCNet increases when \(F_{1}\) and \(K_{E}\) increase. The average accuracy of EEG-TCNet drops when it increases \(F_{T}\) to 12 while increasing \(F_{T}\) to 14 improves the average \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Type** & **Filter** & **Kernel** & **Output** \\ \hline \multirow{3}{*}{\(\phi^{1}\)} & Input & & & (\(1,C,T\)) \\ \cline{2-5}  & Conv2D & \(F_{1}\) & (\(1,K_{E}\)) & \\ \cline{2-5}  & BatchNorm & & & (\(F_{1},C,T\)) \\ \hline \multirow{3}{*}{\(\phi^{2}\)} & DepthwiseConv2D & \(F_{1}\cdot 2\) & (\(C,1\)) & \\ \cline{2-5}  & BatchNorm & & & \\ \cline{2-5}  & EluAct & & & \\ \cline{2-5}  & AveragePool2D & & & \\ \cline{2-5}  & Dropout & & & \\ \hline \multirow{3}{*}{\(\phi^{3}\)} & SeparableConv2D & \(F_{2}\) & (\(1,16\)) & \\ \cline{2-5}  & BatchNorm & & & (\(F_{2},1,T/8\)) \\ \cline{2-5}  & EluAct & & & \\ \cline{2-5}  & AveragePool2D & & & (\(F_{2},1,T/64\)) \\ \cline{2-5}  & Dropout & & & \\ \hline \multirow{3}{*}{\(\phi^{4}\)} & TCN & \(F_{T}\) & \(K_{T}\) & \(F_{T}\) \\ \cline{2-5}  & Dense & & & \\ \cline{2-5}  & SoftMaxAct & & & \\ \hline \hline \end{tabular}  
\(C\)—number of EEG channels, \(T\)—number of time points, \(F_{1}\)—number of temporal filters, \(F_{2}(=2\cdot F_{1})\)—number of spatial filters, \(F_{T}\)—number of filters in TCN module, \(K_{E}\)—kernel size in first convolution, \(K_{T}\)—kernel size in TCN module.  
\end{table} 
Table 1: EEG-TCNet architecture [18]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**EEGNet module** & \(F_{1}\) & \(6,8,10\) \\ \cline{2-4}  & \(K_{E}\) & \(16,32,48\) \\ \hline \multirow{3}{*}{**TCN module**} & \(F_{T}\) & \(10,12,14\) \\ \cline{2-4}  & \(K_{T}\) & \(4,6,8\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiment EEG-TCNet with different hyperparameters. accuracy of EEG-TCNet. Increasing \(K_{T}\) to 6 also improve the accuracy, but the performance declines when increasing \(K_{T}\) to 8. The detailed accuracy of 9 subjects is in Table 11.  
After training 81 experiments, a fixed result and a variable result are selected. In terms of the fixed result, the best hyperparameters \(F_{1}=10,K_{E}=48,F_{T}=12,K_{T}=8\) for all 9 subjects are selected based on the average accuracy. Regarding the variable result, the best hyperparameters \(F_{1},K_{E},F_{T},K_{T}\) for each subject are selected. Table 4 shows the hyperparameters of the variable model. Table 5 compares the accuracies between the baseline model, fixed model, and variable model. The fixed model is better than the baseline model in subject 2, subject 4, subject 6, and subject 9, whereas others are nearly equal. When each subject is selected as the best result in the variable model, the final performance is much higher than the baseline model, especially for subject 2, subject 4, subject 5, subject 6, and subject 9.