Robust EEG-Based Emotion Recognition using CNN: A High-Accuracy Approach with Differential Entropy Features and Spatial-Frequency Domain Analysis on the SEED Dataset

Abstract

The area of Human Emotion Recognition using EEG signals is rapidly evolving its dimensions at a more excellent pace and with time, it has become an important area of research for affective computing in the field of neuroscience. Neuro-computing has also shown its potential applications in the domain of mental health monitoring, brain-computer interface, and adaptive learning systems. The deep learning models have shown significant progress in producing effective results when implemented in analyzing different EEG signals. In this study, the efficiency of Convolutional Neural Network (CNN) models for emotion categorization is investigated on an EEG-based SEED dataset. Differential Entropy (DE) characteristics derived from five important EEG rhythms—delta, theta, alpha, beta, and gamma—are used as inputs to CNN classifiers. To enhance the performance, the model uses a two-dimensional (2D) tensor representation of the input, which allows the network to learn and use spatial correlations between different EEG channels. Experimental results show that the proposed CNN-based strategy outperforms previous methods with an average accuracy of 94.09 %. These findings highlight the potential of CNNs in developing robust and scalable solutions for EEG-based emotion recognition, providing a path for more intuitive and adaptive systems in future applications.