Deciphering Feelings: Exploring EEG Data for Human Emotion Recognition

The ability to decipher human emotions through mind reading techniques has long been a subject of fascination, both in science fiction and scientific research. In recent years, advancements in neuroscience, artificial intelligence, and brain-computer interfaces have brought us closer to understanding and recognizing human emotions from brain activity. Recognizing and understanding human emotions has evolved significant attention in fields like psychology, technology, and healthcare. Electroencephalogram (EEG) signals, reflecting brain activity, offer valuable insights into the neural correlates of emotions. This paper reviews recent advancements in human emotion recognition from EEG signals, focusing on both deep learning and machine learning approaches. Deep learning methods, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise in automatically extracting features from EEG data and accurately classifying emotions. This study explores methods to recognize human emotions using EEG data, focusing on the use of deep learning models to classify emotional states with high accuracy. Meanwhile, machine learning techniques such as support vector machines (SVMs) and ensemble methods have also been employed for emotion recognition tasks. Various studies utilizing different datasets and methodologies are discussed, highlighting the effectiveness of these approaches in emotion categorization. The review also discusses research gap like minimizing noise, extracting relevant features, and achieving real-time processing in EEG-based emotion recognition. It highlights potential avenues for future research in this area. The proposed approach offers insights into the applicability of EEG for real-time emotion analysis. Moreover, the review emphasizes the significance of EEG in deepening our comprehension of human emotions and its wide-ranging applications in fields such as human-computer interaction, healthcare, and education.