As intelligent human-computer interaction (HCI) evolves, the ability of systems to accurately perceive and respond to human emotions has become increasingly crucial. Emotional perception allows machines to adapt and react empathetically, making interactions more natural and engaging. This paper reviews current EEG-based emotion recognition techniques, focusing on key steps such as preprocessing, feature extraction, and machine learning models. Specifically, we explore various models like Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Deep Belief Networks (DBN), all of which have demonstrated promising results in classifying emotional states from EEG signals. In addition, we compare some of the most recent approaches in the field, including MCDDAa method developed at Hebei University of Technology. This technique addresses the challenge of cross-subject adaptation, where recognising emotions in new individuals, not seen during training, is crucial for real-world applications. Many emotion recognition systems struggle with generalizing to new subjects due to individual differences in brainwave patterns. MCDDA attempts to solve this problem, making the technology more robust and scalable.
Linlin Su (Tue,) studied this question.