## 4 Results for P300 Identification
### Fixed Convolution Patches  
Producing good results by mixing information from two adjacent channels in a mapped channel vector was considered with skepticism. However, if a direct comparison between the structures with different pool patches is considered (see Table 2), in all cases but one for the one-layered structures and  
\begin{table}
\begin{tabular}{c|c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**500 ms**} & \multicolumn{3}{c}{**400 ms**} & \multicolumn{3}{c}{**300 ms**} & \multicolumn{3}{c}{**200 ms**} \\ \hline
**CP** & **PP** & **CL 1** & **CL 2** & **CL 1** & **CL 2** & **CL 1** & **CL 2** & **CL 1** & **CL 2** \\ \hline \multirow{3}{*}{\([2\times 1]\)} & \([2\times 1]\) & 0.882 & 0.915 & 0.860 & 0.914 & 0.850 & 0.903 & 0.842 & 0.880 \\  & \([3\times 1]\) & 0.920 & 0.910 & 0.899 & 0.906 & 0.865 & 0.883 & 0.880 & 0.891 \\  & \([3\times 2]\) & 0.907 & 0.910 & 0.919 & 0.906 & 0.881 & 0.884 & 0.897 & 0.887 \\ \hline \multirow{3}{*}{\([4\times 1]\)} & \([2\times 1]\) & 0.855 & 0.869 & 0.867 & 0.880 & 0.796 & 0.809 & 0.837 & 0.814 \\  & \([3\times 1]\) & 0.880 & 0.884 & 0.901 & 0.864 & 0.858 & 0.855 & 0.838 & 0.832 \\  & \([3\times 2]\) & **0.927** & 0.916 & 0.912 & 0.868 & 0.872 & 0.836 & 0.911 & 0.848 \\ \hline \multirow{3}{*}{\([5\times 1]\)} & \([2\times 1]\) & 0.869 & 0.840 & 0.855 & 0.867 & 0.805 & 0.827 & 0.841 & 0.783 \\  & \([3\times 1]\) & 0.896 & 0.847 & 0.880 & 0.868 & 0.874 & 0.839 & 0.841 & 0.826 \\ \cline{1-1}  & \([3\times 2]\) & 0.897 & 0.857 & 0.895 & 0.859 & 0.890 & 0.824 & 0.915 & 0.820 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summarized results for the 18 convolutional neural networks (CNN) structures for all considered trial lengths with fixed convolution patches. PP = pool patch, CP= convolution patch, CL= convolution layers.  
\begin{table}
\begin{tabular}{c|c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**500 ms**} & \multicolumn{3}{c}{**400 ms**} & \multicolumn{3}{c}{**300 ms**} & \multicolumn{3}{c}{**200 ms**} \\ \hline
**PP** & **CP** & **CL 1** & **CL 2** & **CL 1** & **CL 2** & **CL 1** & **CL 2** & **CL 1** & **CL 2** \\ \hline \multirow{3}{*}{\([2\times 1]\)} & \([2\times 1]\) & 0.882 & 0.915 & 0.860 & 0.914 & 0.850 & 0.903 & 0.842 & 0.880 \\  & \([4\times 1]\) & 0.855 & 0.869 & 0.867 & 0.880 & 0.796 & 0.809 & 0.837 & 0.814 \\  & \([5\times 1]\) & 0.869 & 0.840 & 0.855 & 0 considering all trial lengths, the best results were obtained by those models using the pool patch [\(3\times 2\)], which considers both spatial and temporal information. This behavior is not seen for models that use two layers. Additionally, regardless of the number of layers, 58.3% (42 models) of the time, the best results were from structures that used pool patch [\(3\times 2\)], 25% (18 models) of the time for when pool patch [\(3\times 1\)] was applied and 16.6% (12 models) of the time for structures that used pool patch [\(2\times 1\)].  
With respect to the convolution patch [\(5\times 1\)] for trials 200 ms long, the lowest accuracy corresponded to the structure with one layer and pool patch [\(2\times 1\)]. In this condition, an accuracy rate of 0.915 was also achieved by another structure (one layer and pool patch [\(3\times 2\)]), thus representing the biggest accuracy rate gap (around 13%) among results produced for a trial of the same length and convolution patch.