## 4 Discussion  
In our research, we integrated the ResNet18 network with \(DE\) to classify EEG-based human emotion states. \(DE\) serves as a valuable tool for quantifying the uncertainty and information content within EEG signals, aiding in the discernment of distinct signal types. ResNet18, renowned for its deep convolutional architecture, boasts formidable feature extraction capabilities. When EEG signals are fed into the ResNet18 network, it adeptly extracts high-level abstract feature representations, enabling the acquisition of intricate insights into the brain activity's spatiotemporal dynamics. Our findings from emotion recognition using EEG signals underscore the synergistic benefits of this combination, as it enhances both the accuracy and robustness of signal classification through sophisticated EEG signal-processing techniques. To demonstrate the superior performance of the ResNet18 network in conjunction with the \(DE\) method for distinguishing emotion states, we conducted a comparative analysis with recent studies utilizing the same SEED-V dataset. Our evaluation encompassed studies by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. In [PERSON]'s research, an emotion recognition method termed Joint Distributed Instances Represent Transfer (JD-IRT) was employed. This method integrates two core components: Joint Distribution Deep Adaptation (JDDA), and Instance-Representation Transfer (I-RT). [PERSON]'s study utilized a progressive graph convolution network (PGCN) to learn discriminative EEG features. [PERSON] proposed a Graph Adaptive Semi-supervised Discriminative Subspace Learning (GASDSL) model for EEG-based emotion recognition. [PERSON]'s study utilized Discrete Wavelet Transform (DWT) and Statistical Moments (SM) methods to extract features from EEG signals, and applied Convolutional Neural Networks (CNN) for classification. [PERSON]'s study employed the Deep Canonical Correlation Analysis (DCCA) method for emotion recognition from EEG signals. [PERSON]'s study proposed two dimension reduction algorithms based on Local Binary Pattern (LBP) derived from machine learning models, which included electroencephalogram channel selection and conflict learning. Comparing the results of EEG-based emotion classification within the SEED-V dataset, as depicted in Table 2, the accuracies of EEG emotion classification for [PERSON]'s, [PERSON]'s, [PERSON]'s, [PERSON]'s, [PERSON]'s, and [PERSON]'s studies were 65.91%, 71.40%, 82.03%, 82.06%, 85.30%,  
Figure 8: ROC curves of each of the five emotions in Subject 4 compared to other emotions. and 93.85%, respectively. In contrast, our proposed method, which combines the ResNet18 network with \(DE\), achieves an accuracy of 95.61% in classifying EEG-based emotion states, significantly enhancing the resolution performance. Through this comparison of classification accuracy, it becomes evident that our method retains more pertinent information while eliminating redundant data, thus leading to a more precise and efficient EEG emotion classification outcome. However, our research still faces several limitations. [PERSON] et al. [20] segmented the beta band into low and high beta bands and explored their relationship with emotional processing. [PERSON] et al. [21] suggested that different frequency ranges within the beta band significantly influence motor control. [PERSON] et al. [22] identified increased synchronization among motor units involved in stiffness modulation, supported by cross-muscle coherence analysis, revealing peaks in the \(\beta\)-band, typically linked with cortical activity. These studies are about the impact of frequency band separation on brain activity, and we have not yet considered this factor in our experiments. We plan to incorporate frequency band separation considerations in future experiments. Additionally, [PERSON] et al. [23] proposed that posture affects emotional experiences similarly to facial expressions, while [PERSON] et al.