## 4 Materials and Experimental Results  
### Dataset  
The DEAP dataset comprises EEG signals from 32 participants, and the acquisition process is illustrated in Figure 9. During data collection, the \"10-20\" international standard 32-lead electrode cap is used to record signals, and each participant watches 40 one-minute videos while EEG signals are recorded for 63 s (3 s baseline + 60 s of video stimulation) per sample. Thus, the entire dataset consists of 1280 (32 \(\times\) 40) samples, with each sample containing 63 s of data from 32 channels. Following video viewing, participants subjectively evaluated the videos based on arousal, valence, dominance, and liking using a 1-9 scale. Two versions of the DEAP dataset are officially available: one is the raw signal containing noise such as electromyography (EMG) and electrooculogram (EOG); the other is the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Test Accuracy (\%)** & \multirow{2}{*}{**F1-Score**} & **Test Accuracy (\%)** \\  & **(2-Class)** & & **(4-Class)** \\ \hline
64 & 95.09 & 93.47 & 86.33 \\
128 & 95.42 & 94.35 & 87.27 \\
256 & 97.84 & 96.61 & 88.46 \\
512 & 96.57 & 94.66 & 87.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental comparison of different numbers of memories.  
Figure 8: Time feature extraction module.  
preprocessed data, which consists of downsampling the data to 128 Hz from 512 Hz, filtering and denoising using a 4-45 Hz bandpass filter. This study utilizes the preprocessed version of the DEAP dataset in Python to conduct the experiment. The downsampling operation considerably reduces the computational effort, and the resulting accuracy impact is minor.  
Upon removing the baseline from the dataset's signal, a single channel of 60 s will result in 60 \(\times\) 128 samples, and the resulting \(\mathsf{X_{trial,mov}}\) obtained from the 32 channels for a subject will be of the form 32 \(\times\) 60 \(\times\) 128. To create more samples, the data were segmented in this study into nonoverlapping windows with a duration of \(\mathsf{u}\) = 5 s. Specifically, the data were partitioned into 12 windows of 5 s duration, and the data form of \(\mathsf{X_{S}^{i}}\) was 32 \(\times\) 5 \(\times\) 128. The follow-ups were all performed with each window fragment \(\mathsf{X_{S}^{i}}\) as a single experimental sample for emotion recognition. The data form is expanded to 32 \(\times\) 5 \(\times\) 128 \(\times\) 4 by dividing \(\mathsf{X_{S}^{i}}\) into frequency bands. To capture the hidden temporal messages of the sequence signal, the single-band data \(\left\{\mathsf{p_{i}^{\theta}},\mathsf{P_{i}^{\alpha}},\mathsf{P_{i}^{\beta}},\mathsf{P_{i}^{\gamma}}\right\}\) in \(\mathsf{X_{S}^{i}}\) are separated into frames of 0.5 s, and each frame has the data form 32 \(\times\) 0.5 \(\times\) 128 \(\times\) 4. The vector data are converted into a two-dimensional matrix with a sensitive transformation on the basis of a frame, and the DE and PSD features of every 0.5 s frame data are used as matrix element data. Each frame of a single frequency band becomes a 9 \(\times\) 9 matrix, combining two groups of features as well as four frequency bands with different features to obtain the final frame data in the form of 9 \(\times\) 9 \(\times\) 8. The whole sample \(\mathsf{X_{S}^{i}}\) is transformed into a four-dimensional feature sequence of 9 \(\times\) 9 \(\times\) 8 \(\times\) 10 when viewed from the perspective of the whole sample \(\mathsf{X_{S}^{i}}\). Each subject has 40 (video) \(\times\) 60 (seconds) of EEG data, and using 5 s as a sample, 480 copies (40 \(\times\) 12) are generated. For 32 subjects, a total of 15,360 samples are generated.