## 3 Results  
In our experiment, we commenced by computing the confusion matrix using EEG data from 16 participants in the SEED-V dataset, thereby elucidating the precise classification scenario. Subsequently, our investigation encompassed the classification of five classes of human emotions (happiness, sadness, fear, disgust, and neutral) based on EEG signals from the aforementioned 16 individuals, followed by an in-depth analysis of the classification outcomes. To visually represent the classification of EEG emotion states, we undertook a two-dimensional mapping of the classification results and portrayed them via graphical imagery. Additionally, we computed and showcased ROC curves for the five categories of emotional states, thereby facilitating a comprehensive assessment of the efficacy of the proposed network classification model. We utilized the proposed network to classify the five EEG emotional states of 16 participants in the dataset and conducted a comparative analysis of the results. Figure 5 illustrates the classification results for the emotional states of the 16 subjects, with accuracy values spanning from 87.64% to 99.21%. Notably, the 5 th individual attained the lowest classification accuracy, while the remaining 15 individuals achieved accuracies exceeding 90%, with 11 individuals surpassing the 95% mark. In addition to demonstrating recognition accuracy, we present the confusion matrix of the proposed method applied to the SEED-V dataset in Figure 6. This matrix provides insights into the recognition accuracy of the ResNet18 network across different emotional states. Within the confusion matrix, varying color depths indicate different values, transi  
Figure 5: Classification results for the emotional states of the 16 subjects. tioning from blue to red with increasing magnitude. The deeper the red color, the higher the corresponding value. As illustrated in Figure 6, the ResNet18 network achieves recognition accuracies of 86.16%, 90.19%, 94.61%, 96.78%, and 96.13% for the following five emotional states: fear, sadness, neutral, happiness, and disgust, respectively. It is evident that the network exhibits the highest accuracy in recognizing a happy emotional state, while its performance is comparatively lower for fearful emotional states. The accuracy rate for detecting fear emotions is 86.16%, with misclassifications as follows: 3.46% as sad, 1.73% as neutral, 1.8% as happy, and 6.85% as doubt. Notably, fear often tends to be misclassified as doubt due to the similarity in their EEG signals, posing a challenge for accurate differentiation. Moreover, the graphical representation reveals that 2.5% and 0.72% of happy emotional states are misclassified as neutral and disgust emotional states, respectively. To further underscore the efficacy of the ResNet18 network in EEG emotion classification, we employed a two-dimensional visualization of the classification outcomes. The 512 dimensional features output by the average pooling layer of ResNet18 are mapped to a two-dimensional image using the t-SNE algorithm [14]. Different colors represent different emotions, and different emotions are completely separated without overlapping areas, indicating excellent classification results for the five emotions. Illustrated in Figure 7a-d, we depict the classification results for subjects 2, 5, 6, and 9 utilizing the ResNet18 network across five distinct EEG emotional states. Initially, emotions in EEG recordings appear intertwined and challenging to discern. However, post-training and classification using the ResNet18 network, Figure 6 reveals a distinct clustering of emotions of the same type. This visual representation demonstrates that EEG signals corresponding to different emotions are more visually distinguishable, thereby corroborating the effectiveness of the ResNet18 network model in EEG emotion classification. Figure 6: Confusion matrix of different emotion states on the SEED-V dataset.