FPGA-Based Automated EEG Analysis for Efficient Emotion Detection

The recognition of emotions has significant importance in the domains of human-computer interaction and affective computing. The integration of facial expressions, voice analysis, and EEG data in multimodal techniques has shown potential in improving the accuracy of emotion identification. In this study, we provide a novel approach for emotion identification by incorporating facial expression analysis, voice processing, and EEG data analysis into a multimodal system. Machine learning methods are used by the system to classify emotions. The findings of our study indicate that the suggested multimodal technique yields a notable level of classification accuracy across many emotion categories, namely Happy (85%), Sad (75%), Angry (80%), and Neutral (70%). In conclusion, the incorporation of many modalities has been shown to improve the accuracy of emotion identification in comparison to unimodal methodologies. This study highlights the efficacy of multimodal emotion detection systems in capturing a wide range of emotional states, hence facilitating advancements in human-computer interaction and affective computing applications.