# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 5 Conclusions  
This study investigates the fundamental components of database construction and information collection in order to develop an EEG-based emotion detection system and investigate using facial expressions to enhance recognition efficiency. EEG is the recommended method for emotion recognition analysis because of its superior temporal resolution, non-intrusiveness, and capacity to monitor brain activity linked to emotions. The EMOTIV EPOC+ headset was selected due to its wireless capability using Bluetooth, ease of use, and adaptability to many real-world scenarios. These properties also help in this study's wide frequency band collection and cost criteria. In order to elicit a range of emotional reactions, participants are shown short video clips with different emotional content. During the experiment, facial expressions were recorded and monitored using webcam while an EEG EMOTIV EPOC+ headset was used to record EEG signals from 14 different electrode locations placed on different brain lobes. Moreover, in this study, we have proposed the method of integrating the two outputs resulted from a facial expression-based CNN and an EEG signal-based CNN to enhance the overall emotion recognition system efficiency using decision-level fusion technique depending on confidence score values. This method has increased the recognition accuracy from 87.43% with only the EEG data represented as a brain activity map to 91.21% with the combined CNN output by including DeepFace CNN. Each CNN is designed and trained to recognize visual characteristics from pictures of facial expressions and EEG data presented as a topography in order to capture human emotional states and create a complete high-efficiency system that can recognize emotional states. Such an emotion recognition system is more efficient than most systems based on single data modalities in terms of accuracy and generalization capabilities. These advancements pave the way for future technology such as emotion-aware virtual assistants, immersive interactive entertainment, and attentive mental health monitoring, which will all be seamlessly integrated. For future work, it is planned to delve deeper into the exploration of alternative fusion techniques. This includes conducting comparative studies between decision-level, feature-level, and hybrid fusion approaches to determine their respective advantages and limitations in the context of emotion recognition. Additionally, we aim to investigate novel fusion strategies that leverage advanced machine learning algorithms, such as deep learning architectures, to further improve the performance of our multimodal emotion recognition system. By systematically evaluating these fusion methods, we anticipate refinement of our system's capabilities and contribution to the advancement of multimodal affective computing research. The extraction of different features from EEG signals for emotion recognition is also to be investigated in future work. In addition to this, there will be more analysis on other emotions included in the database, taking into consideration the ethical aspects and privacy concerns. In conclusion, this research at the confluence of affective computing, machine learning, and computer vision is highly promising and spurs further exploratory studies. This merging of neural networks is set to enhance the development of human emotion recognition methods that are more intuitive, responsive, and emotionally intelligent. The system was trained on a self-collected database with instant emotion reporting. The data collected are meant to solve a very common problem, which is the reporting of one single emotion with one specific emotional degree per stimulus or session of the experiment. In the collected database, this problem is solved by asking the participant to report the emotions instantly, and whenever it changes, the participant is asked to update it.