# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 3 Numerical Results
### Data Results and Analysis of Subjective Evaluation Experiments
#### 3.1.2 Brain Wave Data Statistics and Result Analysis  
First, the EEG data were processed by the EEGLAB toolbox in MATLAB and the Neuron-Spectrum software. The data were segmented based on the duration of audiovisual stimuli, and prominent artifacts caused by blinking, eye movement, and muscle activity were removed. After obtaining clean EEG signals, the power spectral density of theta (4 Hz-8 Hz), alpha (8 Hz-13 Hz), and beta (13 Hz-30 Hz) rhythms was calculated. A total of 20 participants' EEG data with valid signals were obtained (the subjective evaluation experiment yielded data from the same 20 participants). Due to the interindividual variability in EEG energy, relative power spectral density values were calculated, which involved computing the ratio of the power spectral density of each participant's EEG signals under different audiovisual stimulus conditions to the power spectral density of their reference states. Figure 12: Relative values of the subjective ratings of the 5 video signals on the 3 emotions. The main focus of this work is to observe the trend of changes in EEG energy after adding LFS signals under three known emotion states. Therefore, statistical analysis was conducted on the relative power spectral density values of EEG signals after adding LFS signals compared to the values without audio signals (Table 5 shows the relative power spectral density ratio after adding LFS signals compared to the original silent condition). The results showed significant differences in the effects of LFS signals on EEG energy under the three known emotion states. Overall, the results were consistent across the four electrode locations, which may be related to their shared involvement in emotion-related brain functions. The addition of LFS signals triggered an increase in \(\theta\) and \(\alpha\) rhythm energy associated with known happy emotions, while suppressing \(\beta\) rhythm energy. In contrast, the effects of LFS signals on EEG energy associated with known sad emotions manifested as an increase in \(\theta\) and \(\beta\) rhythm energy, with inconsistent changes observed in \(\alpha\) rhythm energy. When the known emotion state of the participants was shock, the added LFS signals significantly suppressed \(\theta\) rhythm energy, while stimulating \(\beta\) rhythm energy to a large extent, with minimal effects observed on \(\alpha\) rhythm energy based on the results. The change in EEG rhythm energy induced by the addition of each LFS relative to the original video signal under the three known mood conditions is shown in Figure 13. When comparing the results of EEG energy induced by various LFS signals added to the original video signals under three known emotion conditions (averaged across four electrode locations), it was found that the results of the \(\theta\) and \(\beta\) rhythm energy induced \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Emotion** & **Signals** & **Figure 1** & **Figure 2** & **Figure 3** & **Figure 4** & **Animated Figure** \\ \hline \multirow{4}{*}{Happiness} & S1 & \(-\)0.2 & \(-\)0.45 & 0.05 & \(-\)0.25 & \(-\)1 \\  & S2 & \(-\)0.65 & \(-\)0.9 & \(-\)0.25 & \(-\)1.15 & \(-\)1.05 \\  & S3 & \(-\)0.95 & \(-\)1.15 & \(-\)0.65 & \(-\)1.65 & \(-\)1.7 \\ \hline \multirow{4}{*}{Sadness} & S4 & 0.15 & 0.05 & 0.1 & 0 & 0 \\  & S5 & 0.2 & 0.35 & 0.1 & 0.2 & 0.3 \\  & S6 & 1.05 & 0.6 & 0.9 & 0.35 & 0.85 \\ \hline \multirow{4}{*}{Shock} & S7 & 0.35 & 0.3 & \(-\)0.1 & 0.55 & 0.05 \\  & S8 & 0.35 & 0.4 & 0.15 & 0.25 & \(-\)0.15 \\ \cline{1-1}  & S9 & 0.3 & 0.75 & \(-\)0.1 & 0.6 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Subjective evaluation relative values of 3 emotion states.