EEG-Based Emotion Recognition Using Morlet Dual-Level Wavelet Contextual Neural Network with Snow Geese Algorithm

EEG-based Emotion Recognition (ER) works by analyzing brainwave patterns from EEG signals to understand and identify a person's emotional state. The problem of emotion identification from EEG signals remains challenging because brain activity presents both non-linear dynamic properties and individual-specific EEG patterns along with biological noise. Traditional approaches to emotion detection become inefficient due to EEG signal complexity thereby complicating accuracy and reliability measures. To tackle the challenges in EEG-based emotion recognition, this study presents a new approach called the Morlet Dual-level Wavelet Contextual Neural Network with Snow Geese Algorithm (MorDWCNNet+SGA). The method uses the SEED dataset, starting with preprocessing through the Observability-Constrained Resampling-Free Cubature Kalman Filter (OCRCKF) to enhance important EEG patterns. For feature extraction, the Multi-Discrete Wavelet Transform (MDWT) is applied to capture key EEG characteristics. Emotion classification is then performed using the Morlet Dual-level Wavelet Contextual Neural Network (MorDWCNNet), which is further optimized by the Snow Geese Algorithm (SGA) to improve accuracy. Implemented in Python and tested on the SEED dataset, the MorDWCNNet+SGA framework achieves remarkable results, with 99.9% accuracy and 99.3% sensitivity, significantly outperforming existing methods. The outcomes of the proposed method achieved high accuracy in effectively distinguishing emotional states through brainwave patterns. This method demonstrates how advanced techniques can combine synergistically to enhance ER performance resulting in improved real-world application accuracy and speed.