EEG-Based Emotion Recognition: A Review and Emerging Paths

Electroencephalography (EEG)-based emotion recognition (ER) is a growing research area in affective computing and human–computer interaction. EEG signals capture non-invasive measurements of brain activity and provide real-time access to emotional processes at the neural level. Compared to facial expressions or speech, EEG is less susceptible to voluntary masking and therefore holds promise for robust emotion decoding. However, traditional EEG-based ER methods are often limited by low classification accuracy, small sample sizes, and reliance on discrete or bipolar affective labels. Recent advances in deep learning, multimodal fusion, and semantic-space emotion models offer promising directions. This paper reviews state-of-the-art EEG-based ER approaches, focusing on signal preprocessing, feature extraction, and classification techniques. In addition, we identify emerging trends and persistent challenges, including dataset generalizability, inter-subject variability, and the need for fine-grained emotion annotation. By synthesizing current methodologies and outlining future paths, this review aims to guide both researchers and practitioners in the development of more accurate, scalable, and cognitively grounded EEG-based emotion recognition systems.