# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
### Emotion Recognition through EEG Signals  
EEG is important in emotion identification because it provides insight into the electrical activity of the brain linked with diverse emotional states. This non-invasive technique records the brain's different activities when the human is exposed to different emotional stimuli in real time, revealing novel insights into the intricate dynamics of emotional processing. EEG data analysis allows researchers to discover precise patterns and variations in brain activity that correlate with various emotions such as happiness, sadness, anger, and fear. These patterns--which are distinguished by different frequency bands such asalpha, beta, and gamma, power spectral density, and event-related potentials--allow the creation of complex algorithms for reliably identifying emotions. Electrodes are applied to the scalp in the EEG technique to assess brain electrical activity. EEG signals are extremely tiny, usually measured in microvolts (\(\upmu\)V). After being amplified and noise filtered, these signals are shown on a computer screen. The frontal, parietal, temporal, and occipital lobes are the four lobes that make up the brain. Different functions, including movement, attention, memory, and vision, are attributed to each lobe. Different cognitive states can be identified from the EEG signals collected from different brain lobes; emotions are generally captured in the frequency range of 0.5-100 Hz, as Table 1 illustrates. Recent research has focused a lot of interest on EEG-based emotion detection because of its potential to shed light on the underlying brain mechanisms that underlie emotions. Electrodes are applied to the scalp to measure electrical brain activity, and thus makes EEG a non-invasive, easily accessible method for analyzing emotions. Comparing EEG signals to various approaches for emotion identification reveals some important advantages. Because EEG-based emotion recognition algorithms have such a high temporal precision, scientists can record the quick changes in brain activity that coincide with transient feelings. EEG's non-intrusiveness and safety make it a preferable choice for repeated brain assessments, posing no risk of side effects. Presently, EEG is easy to use and that allows its usage in various applications in different fields, including medical and non-medical fields. One more advantage of using EEG signals over facial expressions for emotion recognition is its generality of use for different ages groups from infants to elderly. EEG is useful for studying complicated emotional states and social interactions because it provides so much information about the human body. Its applications cover a wide range of real-world situations, highlighting its function in individualized emotion detection and treatment, from focused emotional therapies to emotion recognition in human-computer interactions. Overall, EEG offers significant insights into the neurological aspects of emotions, establishing it as a vital instrument in emotion recognition research. The use of deep learning and machine learning techniques to identify emotional states from EEG data has been the focus of recent research [21; 22]. These methods, like other machine learning methods, start by preprocessing the EEG data in order to remove noise. The next step for classification purposes is to identify important characteristics describing [23; 24; 25] event-related potentials (ERPs), power spectral density (PSD), and time-frequency representations, along with the most widely used characteristics, as described in [26]. Then, these EEG characteristics are used in machine learning algorithms such as SVM, Random Forest, KNN, and others though divide emotional states [27; 28; 29; 30; 31; 32]. Additionally, deep learning models, including LSTM networks and RNNs, have proven