## 4 Experiments and Results
### EEG-Conformer with Different Hyperparameters  
The architecture of the convolution module of EEG-Conformer is described in Table 9.  
Figure 10: The column chart shows the accuracies of baseline EEG-TCNet, EEG-TCNet with a fixed bandpass filter, and EEG-TCNet with a variable bandpass filter.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subject** & **Baseline** & **Fixed Bandpass Filter** & **Variable Bandpass Filter** \\ \hline
**S1** & **82.92** & 79,00 & 82.21 \\
**S2** & **61.13** & 51.94 & 56.18 \\
**S3** & 91.58 & 90.48 & **94.51** \\
**S4** & 64.91 & **72.37** & **72.37** \\
**S5** & **76.45** & 56.88 & 56.88 \\
**S6** & **59.53** & 51.16 & 54.88 \\
**S7** & **91.34** & 82.67 & 84.84 \\
**S8** & 81.92 & 82.29 & **85.24** \\
**S9** & 79.17 & 78.79 & **84.85** \\
**Average** & **76.55** & 71.73 & 74.66 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy of baseline EEG-TCNet and EEG-TCNet with fixed bandpass filter and with variable bandpass filter (%).  
In sub-project 1 with EEG-Conformer, the model is trained with different numbers of temporal filters in convolution module \(k\) and different numbers of self-attention blocks \(N\), described in Table 10. The baseline model in the original study has \(k=40\) and \(N=6\).  
In Table 11, the contribution of each \(k\), \(N\) to the performance of EEG-TCNet is examined by keeping the baseline value of one parameter and changing one parameter. The increasing \(k\) improves the average accuracy, but the performance declines when \(k\) rises from 40 to 60. The average accuracy decreases when \(N\) is from 6 to 7 and then increases slightly when \(N\) is from 7 to 10, but the average accuracy is still lower than \(N\) of 6. The detailed accuracy of 9 subjects is in Table A3.  
After training 25 experiments, the fixed hyperparameters selected are \(k=20\) and \(N=10\). The variable hyperparameters are described in Table 12.  
Table 13 compares and visualises the accuracies of the EEG-Conformer baseline model, fixed model, and variable models. Their accuracies are nearly equal in each subject. However, subject 2 in the fixed model has higher accuracy than that in the baseline model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(k\) & \(N\) & **Average Accuracy (\%)** \\ \hline
20 & & & 78.66 \\
30 & & & 78.02 \\
40 & 6 & **79.64** \\
50 & & & 77.86 \\
60 & & & 77.74 \\ \hline  & 6 & **79.64** \\  & 7 & 78.27 \\
40 & 8 & 78.50 \\  & 9 & 78.58 \\  & 10 & 78.65 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experiment EEG-Conformer with different hyperparameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Layer**} & **In** & **Out** & **Kernel** & **Stride** \\ \hline Temporal Conv & 1 & \(k\) & (1, 25) & (1, 1) \\ Spatial Conv & \(k\) & \(k\) & (\(ch\), 1) & (1, 1) \\ Avg Pooling & \(k\) & \(k\) & (1, 75) & (1, 15) \\ Rearrange & & & (\(k\), \(1\), \(m\)) & (\(m\), \(k\)) & \\ \hline \hline \end{tabular} \(k\)—number of temporal filters in the convolution module; \(ch\)—number of channels in EEG data.  
\end{table}
Table 9: The architecture of the convolution module in EEG-Conformer [26].  
\begin{table}
\begin{tabular}{c c} \hline \hline \(k\) & \(N\) & **Average Accuracy (\%)** \\ \hline
20 & & & 78.66 \\
30 & & & 78.02 \\
40 & 6 & **79.64** \\
50 & & & 77.86 \\
60 & & & 77.74 \\ \hline  & 6 & **79.64** \\  & 7 & 78.27 \\
40 & 8 & 78.50 \\  & 9 & 78.58 \\  & 10 & 78.65 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of different numbers of filters \(k\) and numbers of self-attention blocks \(N\) in EEG-Conformer in average accuracy.  
Figure 11 shows a comparison of accuracies between baseline EEG-Conformer, EEG-Conformer with the best tuning hyperparameters (optimised fix model), and EEG-Conformer with the best hyperparameters of each subject from different trainings. This experiment aims to research the impact of different hyperparameters on EEG-Conformer. This is evidence that different hyperparameters do not enhance EEG-Conformer.