## 4 Discussion  
In this study, we evaluated the effectiveness of transposed convolution in classifying BCI data and compared the results with previous studies. As a measure of accuracy, we employed [PERSON]'s kappa, which has been widely used in prior studies as a simple 0-1 metric that provides a linear mapping of the classification accuracy from chance level to 100%. Thus, comparing kappa values is equivalent to comparing the classification accuracy. Although the EEGNet paper does not report a specific accuracy metric, our results suggest that the approximate accuracy of the readings, when expressed as a kappa value, is slightly lower than that of the conventional method. However, by transforming EEG data into a 2D map using transposed convolution, we achieved a high level of accuracy comparable to previous methods, demonstrating the efficacy of the transposed convolution approach. The conventional method for transforming a signal into a 2D map is the Short-Time Fourier Transform (STFT). STFT is commonly employed in EEG analysis, and it extends the frequency axis by a number equal to the predetermined window width. However, STFT only extracts frequency information at equal intervals, with no guarantee of the validity of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(Ch_{out}\) = 8 ch} & \multicolumn{5}{c}{\(W\)} \\ \cline{2-6}  & **4** & **8** & **12** & **16** \\ \hline \multirow{4}{*}{\(R\)} & 50 & 0.7326 & 0.7641 & 0.7571 & 0.7747 \\ \cline{2-6}  & 100 & 0.7559 & 0.7587 & 0.7673 & 0.7606 \\ \cline{2-6}  & 150 & 0.7513 & 0.7754 & 0.7762 & \\ \cline{2-6}  & 200 & 0.7419 & 0.7704 & \\ \cline{2-6}  & 250 & 0.7493 & 0.7774 & \\ \hline \end{tabular} The diagonal line represents data not tested because of the computational costs.  
\end{table} 
Table 10: Accuracy of parameter search at \(Ch_{out}\) = 8 ch. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(Ch_{out}\) = 12 ch} & \multicolumn{5}{c}{\(W\)} \\ \cline{2-5}  & **4** & **8** & **12** & **16** \\ \hline \multirow{4}{*}{\(R\)} & 50 & 0.7474 & 0.7556 & 0.7727 & 0.7684 \\ \cline{2-5}  & 100 & 0.7466 & 0.7684 & \\ \cline{2-5}  & 150 & 0.7505 & & \\ \cline{2-5}  & 200 & 0.7505 & & \\ \cline{2-5}  & 250 & 0.7485 & & \\ \hline \end{tabular} The diagonal line represents data not tested because of the computational costs.  
\end{table} 
Table 11: Accuracy of parameter search at \(Ch_{out}\) = 12 ch. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(Ch_{out}\) = 16 ch} & \multicolumn{5}{c}{\(W\)} \\ \cline{2-5}  & **4** & **8** & **12** & **16** \\ \hline \multirow{4}{*}{\(R\)} & 50 & 0.7474 & 0.7556 & 0.7727 & 0.7684 \\ \cline{2-5}  & 100 & 0.7466 & 0.7684 & \\ \cline{2-5}  & 150 & 0.7505 & & \\ \cline{2-5}  & 200 & 0.7505 & & \\ \cline{2-5}  & 250 & 0.7485 & & \\ \hline \end{tabular} The diagonal line represents data not tested because of the computational costs.  
\end{table} 
Table 12: Accuracy of parameter search at \(Ch_{out}\) = 16 ch. the information in this range. The proposed method employs transposed convolution to increase the signal along a different axis direction with a predetermined window width. The integration of preprocessing into the neural network framework enables the determination of the window width and the number of extensions to be made arbitrarily, thus facilitating the learning of features that are instrumental for successful classification. The reason we used transposed convolution is to extract features from EEG by unfolding it. Transposed convolution is designed to perform the inverse operation of normal convolution and is expected to decompose complexly convolved data. We found that CNN using transposed convolution can classify data more accurately than CNN without using it or using STFT data for classification. Therefore, we concluded that a kernel that unfolds EEG while capturing its features has been learned through the use of transposed convolution.