## 4 Materials and Experiments
### Experimental Comparison
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Methods** & **Recognition** & **Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline \multirow{4}{*}{LSTM} & H/L Valence & & & \\  & H/L Arousal & & & \\  & H/L Dominance & & & \\  & Like/Unlike State & & & \\ \hline \multirow{4}{*}{BiLSTM} & H/L Valence & & & \\  & H/L Arousal & & & \\  & H/L Dominance & & & \\  & Like/Unlike State & & & \\ \hline \multirow{4}{*}{CNN-LSTM} & H/L Valence & & & \\  & H/L Arousal & & & \\  & H/L Dominance & & & \\  & Like/Unlike State & & & \\ \hline \multirow{4}{*}{CNN-BiLSTM} & H/L Valence & & & \\  & H/L Arousal & 90.12 & 95.05 & 12.06 \\  & H/L Dominance & & & \\  & Like/Unlike State & & & \\ \hline \multirow{4}{*}{CNN-BiLSTM-Attention} & H/L Valence & & & \\  & H/L Arousal & 92.25 & 86.20 & 11.47 \\ \cline{1-1}  & H/L Dominance & & & \\ \cline{1-1}  & Like/Unlike State & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with the base model. future emotional states in EEG time series signals, accuracy is still low. The DE-CNN-BiLSTM [12] fully considers the complexity and spatial structure of the brain and considers the temporal properties of dynamic EEG signals. However, there is no learning of time-frequency features, and no self-attention mechanism is used to redistribute the weights of EEG emotional key information. There is no convolutional smoothing signal processed by CNN in LSTM-Attention [14], no resolution of spatial structure, just resolution of a single time series with attention. In contrast to these studies, the CP-CNN-BiLSTM-MHSA method proposed in this paper improves the spatial information in the EEG signal, and the features in the time-frequency domain are analyzed and then input to the network as a time-frequency map. This feature information was re-extracted using CNN, for the dynamic temporal features present in the EEG, and then the future and past key sentiment information in it was fully learned using BiLSTM. Finally, the weight of this information is redistributed using a self-attentive mechanism, making the network more capable of recognizing this information. In the comparison of binary classification in the DEAP dataset, the proposed method in this paper is significant in sentiment recognition research enhancement. The results of the four-classification experiment were compared with the previous two-dimensional potency-arousal four-classification studies of HALV, HAHV, LALV, and LAHV using the DEAP dataset, and the results are shown in Table 9. The CNN-LSTM [18] fuses the spatial, frequency domain, and temporal features of the original EEG signal, but the recognition accuracy is still low for the four-classification tasks. The SVM [22; 23; 24] used wavelets to decompose and extract the time-frequency features and smoothed the features into the SVM but did not improve the spatial information and dynamic time features of the EEG. The PSO-BiLSTM [19] deep learning techniques based on long and short-term memory are used to retrieve emotional changes from optimized data corresponding to labeled EEG signals and to remove repetitive information, but this also lacks learning of spatial and time-frequency features. The WP-KNN [13], on the other hand, does not analyze the dynamic time features. In summary, the method proposed in this paper fully analyzes the dynamic temporal features and spatial information present in EEG and is effective in four-category sentiment recognition. Figure 11: Model comparison chart. \begin{table}
\begin{tabular}{c} CP-CNN- \\ BiLSTM- \\ MHSA \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with previous two-classification studies.