EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network

Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human-computer interaction, especially in adaptive systems. This paper proposes a novel emotion recognition system that improves classification accuracy through advanced signal processing, adaptive channel selection, and deep learning techniques. The system starts with the multi-scale wavelet transform to break down EEG signals effectively followed by Kalman filtering with Wavelet denoising that enhances the quality of signal. Emotional peaks are established using Spectral Entropy analysis for accurate epoch determination. For adaptive channel selection, the utilization of Reinforcement based Deep Q-Networks to pick the most informative EEG channels dynamically for each emotional state. In feature extraction, the hybrid model based on Spatio-Temporal Attention Networks (ST-ANs) is adopted to capture spatial and temporal dependencies in the EEG data. Multi-Scale Feature Fusion is utilized to fuse short- and long-term dependencies. Final emotion classification is done through an Ensemble of Graph Neural Networks (GNNs) and Memory-Augmented Neural Networks (MANNs) providing robust adaptability across different subjects and emotional states. The benchmark dataset is collected from kaggle repository such as EEG brainwave, DEAP dataset and Computer game based EEG dataset. The proposed work achieves 98.5% accuracy on the EEG Brainwave Dataset. On the DEAP Dataset, it achieves 94.5% accuracy. For the EDA on Emotion Recognition (S01G1AllChannels), the model reaches 97.6% accuracy, outperforming the existing frameworks in emotion classification.