Decoding Emotions Using Deep Learning Approach to EEG-Based Emotion Recognition

In disciplines including medicine, psychology, and human-computer interaction, understanding and interpreting human emotions is crucial. Emotion analysis that is accurate and real-time has the potential to transform these fields. Deep learning methods combined with Electroencephalography (EEG) inputs have become a viable approach for enhancing emotion identification systems in recent years. With EEG data and a cutting-edge Deep CNN algorithm, this study introduces a novel method for emotion analysis that achieves a remarkable accuracy rate of 96.24% and model loss as 0.89. We gathered EEG data from a variety of participants, drepresenting a spectrum of emotional reactions. EEG data are a useful source for emotion research because they provide clear insights into the electrical activity of the brain.In our study, EEG data is processed using a Deep Convolutional Neural Network (CNN) architecture, which is well-known for its efficiency in image-related tasks. This deep learning algorithm improves the precision of emotion identification by learning complex spatial and temporal patterns from EEG data.The creation of a novel feature extraction technique specifically designed for EEG data is a key breakthrough in our strategy. By capturing both spectral and temporal properties, this technique gives the model additional discriminative data for classifying emotions.A great accuracy rate of 96.24% was attained by the suggested technique while classifying emotions. This extraordinary precision indicates the dependability and strength of our method. Real-time emotion detection is made possible by our model's quick analysis of EEG data, which has implications for virtual reality, human-computer interaction, and mental health monitoring, among other fields. In order to further our understanding of how the human brain reacts to emotional inputs, we offer insights into the neural patterns connected to various emotions.This study illustrates the application of deep learning, more especially the Deep CNN algorithm, to emotion analysis using EEG data. With the help of this cutting-edge architecture and our innovative feature extraction technique, accuracy has greatly increased, making it a useful tool for situations where accurate emotion identification is essential. This discovery has ramifications across a range of fields, including psychology, healthcare, and human-computer interaction, eventually improving our capacity to recognize and react to emotional states in others.