# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 3 Multi-Modal System EEG with Facial Expressions--Proposed Model
### EEG-Based Emotion Recognition  
Emotion recognition from EEG signals has gained significant interest in recent years due to its potential applications in various domains, such as human-computer interaction, mental health monitoring, and emotion-driven personalized services. Deep learning, particularly CNNs, has shown promising results in extracting meaningful features from EEG data for emotion classification. EEG is a non-invasive technique that records electrical activity from the scalp, reflecting the underlying neural processes related to cognitive and emotional states. Emotions are believed to manifest distinct patterns in the brain's electrical activity, making EEG an attractive modality for emotion recognition. Deep learning has revolutionized various domains, including computer vision and natural language processing. When applied to EEG emotion recognition, deep neural networks can automatically learn relevant features from raw EEG signals, mitigating the need for hand-engineering features [38]. CNNs are a particular kind of deep neural network and are used to handle data that resemble a grid, such as heat maps or topographies, or time series data like EEG or picture data. CNNs can detect local patterns and feature hierarchies, which are essential for determining spatial relationships in EEG signals, by employing convolutional layers. A heat map depiction of emotion topography depicts the spatial arrangement of brain activations linked to various emotions. Another crucial step in simplifying the entire emotion identification system is determining the quantity of electrodes used to generate the heat maps and its placement locations on the human scalp. Studies frequently use the method of focusing on certain brain regions, such as the amygdala and prefrontal cortex, which are known to be involved in emotional processing [21; 38; 56]. Optimum channel selection can help the system identify emotions more accurately by cutting down on unwanted noise and irrelevant inputs. Furthermore, consistent EEG readings with fewer artifacts and interferences depend on accurate electrode placement. Following the universal 10-20 method for electrode placement guarantees consistent and standardized data gathering between participants, which enhances the performance and comparability of emotion recognition algorithms. The field of EEG-based emotion recognition can be greatly advanced, and the practical applicability of these systems in areas such as affective computing and mental health monitoring enhanced by fine-tuning the selection of channels and electrodes. This will yield more consistent and comprehensible results. During the training process, the model fine-tunes its internal parameters, learning to minimize the classification error. This approach ensures that the model is thoroughly trained on a substantial portion of the available data, providing the model with a comprehensive understanding of the patterns associated with different emotional states as represented in the EEG signals. Evaluation of the model is performed on separate test  
Figure 8: The optimum nine channels selected for valence axis level classification. databases (20%) to assess its generalization and performance on unseen data. These percentages for the data split are used for valence emotion axis level recognition. The EEG emotion recognition system based on CNNs using emotion topography heat map images has numerous potential applications. It can be integrated into real-time emotion-aware systems, affective computing, and mental health monitoring, enabling personalized services based on detected emotional states. Utilizing raw EEG signal recordings obtained via the headset, PSD values are computed at specific, optimized electrode locations, and subsequently employed to generate a cerebral heat map that visualizes patterns of brain activity. The valence CNN architecture presented in Table 3 is designed for the analysis of EEG topographies.