###### Abstract  
The high-accuracy detection of multiple distinct human emotions is crucial for advancing affective computing, mental health diagnostics, and human-computer interaction. The integration of deep learning networks with entropy measures holds significant potential in neuroscience and medicine, especially for analyzing EEG-based emotion states. This study proposes a method combining ResNet18 with differential entropy to identify five types of human emotions (happiness, sadness, fear, disgust, and neutral) from EEG signals. Our approach first calculates the differential entropy of EEG signals to capture the complexity and variability of the emotional states. Then, the ResNet18 network is employed to learn feature representations from the differential entropy measures, which effectively captures the intricate spatiotemporal dynamics inherent in emotional EEG patterns using residual connections. To validate the efficacy of our method, we conducted experiments on the SEED-V dataset, achieving an average accuracy of 95.61%. Our findings demonstrate that the combination of ResNet18 with differential entropy is highly effective in classifying multiple distinct human emotions from EEG signals. This method shows robust generalization and broad applicability, indicating its potential for extension to various pattern recognition tasks across different domains. multiple distinct human emotion; ResNet18; differential entropy +
Footnote †: journal:Moreover, deep learning models can optimize model structures and algorithms, leading to higher running speeds and real-time performance, thus making emotion recognition systems more practical for real-world applications [7]. For instance, the integration of deep learning with EEG technology represents a promising avenue for advancing emotion recognition capabilities and improving their feasibility in practical settings. For example, [PERSON] et al. introduced a Graph Convolutional Network (GCN) based on Ordinary Differential Equations (ODE), which significantly enhances performance in handling the relationships among EEG channels [8]. In another instance, [PERSON] et al. presented a method leveraging spiking neural networks to achieve high accuracy in recognizing emotions within EEG datasets [9]. EEG signals are known for their inherent complexity and nonlinearity, making them challenging to analyze directly. Entropy serves as a valuable metric to quantify this complexity, offering a quantitative measure of the irregularity within the signal [10]. A higher entropy value signifies increased unpredictability and complexity in the signal, while a lower entropy value indicates a more structured and ordered signal. Leveraging entropy as a feature extraction tool facilitates the conversion of raw EEG signals into numerical representations, thereby enabling various applications such as emotion classification and cognitive state recognition [11]. For instance, [PERSON] et al. proposed a novel EEG-based emotion recognition approach that utilizes dynamic entropy measures, demonstrating robust generality in emotion recognition tasks [12]. Additionally, in [PERSON]'s research, a novel classification method was investigated, leveraging Tsallis entropy in conjunction with the K-Nearest Neighbors (KNN) classifier [10]. Examining entropy provides insights into the dynamic characteristics of the nervous system. Differential entropy (\(DE\)) offers a means to capture the temporal variations present in signals. Given that EEG signals exhibit dynamic changes over time, employing \(DE\) proves particularly advantageous, as it offers a more precise depiction of this evolving process. Moreover, \(DE\) surpasses standard entropy by accounting for signal variations over time, thereby enhancing feature discrimination and proving especially beneficial for complex emotional and cognitive state recognition tasks [13]. In our study, we propose a novel approach to train and classify EEG emotion states by integrating ResNet18 network architecture with \(DE\).