## 4 Materials and Experimental Results
### Ablation Experiments  
In Section 3.2, three forms of electrode mapping were proposed to determine the optimal mapping method for the deep learning model presented in this article. The performances of these mapping methods were experimentally compared, and the results are presented in Table 4. The sensitive transformation method, which includes more precise electrode locations and provides spatial information that is more consistent with emotional neural features, yielded the highest accuracy. Furthermore, the time consumption of this method was comparable to that of the compact mapping method and approximately one-third of that of the sparse mapping method. Therefore, considering both time and accuracy, the sensitive transformation method is the most suitable approach. The present study focuses on the recognition task by employing window segments as samples, which depends on the interframe correlation to extract temporal features. The selection of an appropriate time window and frame length is crucial, and thus, this study conducts experiments to determine optimal values. The accuracy of the model is compared for different values of u and t, as presented in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Shape**} & \multicolumn{2}{c}{**Valence**} & \multicolumn{2}{c}{**Arousal**} & \multicolumn{2}{c}{**Valence-Arousal**} \\ \cline{3-8}  & & **Acc (\%)** & **Time (s)** & **Acc (\%)** & **Time (s)** & **Acc (\%)** & **Time (s)** \\ \hline Compact Mapping & 8 \(\times\) 9 & 97.09 & 89 s & 96.85 & 86 s & 87.59 & 93 s \\ Sparse Mapping & 19 \(\times\) 19 & 97.32 & 255 s & 97.06 & 247 s & 88.07 & 262 s \\ (Ours) & 9 \(\times\) 9 & 98.12 & 95 s & 97.72 & 91 s & 88.46 & 103 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental comparison of different mapping methods. Figure 13: Comparison of valence/arousal four-classification experiments. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**DEAP-Valence**} & \multicolumn{3}{c}{**DEAP-Arousal**} & \multicolumn{3}{c}{**Valence-Arousal**} \\ \hline  & t = 0.25 s & t = 0.5 s & t = 1 s & t = 0.25 s & t = 0.5 s & t = 1 s & t = 0.25 s & t = 0.5 s & t = 1 s \\ \hline u = 3 s & 95.87\% & 96.62\% & 94.08\% & 95.85\% & 96.23\% & 93.83\% & 87.98\% & 88.11\% & 85.38\% \\ u = 4 s & 96.83\% & 97.53\% & 95.02\% & 97.15\% & 97.64\% & 94.58\% & 88.12\% & 88.20\% & 85.05\% \\ u = 5 s & 97.43\% & 98.12\% & 95.90\% & 97.30\% & 97.72\% & 95.82\% & 88.30\% & 88.46\% & 85.46\% \\ u = 6 s & 95.82\% & 96.72\% & 95.17\% & 96.57\% & 96.89\% & 94.53\% & 87.95\% & 88.14\% & 85.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental comparison of different time windows and frame lengths. Based on the results, it is apparent that the optimal classification performance for the DEAP dataset was achieved when u = 5 s and t = 0.5 s. Regarding binary valence and arousal, the maximum difference in accuracy was 4.04% and 3.89%, respectively, and the maximum difference in the four classifications was 3.41%. For window segments of different lengths, the increase in the number of frames enables Bi-LSTM to extract richer temporal information. t = 0.25 s/0.5 s will be higher than the accuracy of 1 s frame division for the same window segment. However, when t = 0.25 s, the doubling of the number of frame sequences does not bring a continuous increase in accuracy, and the overall accuracy is close to the experimental data for t = 0.5 s, but the computational effort of the network is greatly increased. Furthermore, although the proposed model approach in this paper showed good performance for both binary and four classifications, the effectiveness of individual modules within the model remains unknown. To address this issue, we designed five models with different structures for ablation experiments, and their compositions and results are presented in Table 6. The task identification of all models is based on 4D features designed in this paper, and the baseline CNN is designed without an attention mechanism.