A Hybrid Fuzzy CNN–LSTM Approach for Emotion Recognition from EEG–ECG Physiological Signals

Emotion recognition from physiological signals is a promising approach in affective computing because it is accurate and less affected by external conditions. This paper proposes a new hybrid model that combines fuzzy logic and deep learning to improve Electroencephalogram (EEG) and Electrocardiogram (ECG)-based multimodal emotion recognition. The system undertakes feature-level fusion of EEG and ECG, coupled with fuzzy logic–based membership scoring for handling uncertainty and subject variability. These fuzzy-enhanced representations are then utilized as input to a hybrid Convolutional Neural Network (CNN)–LSTM model, allowing automatic spatial association extraction and temporal emotional dynamics extraction. When tested on the DREAMER dataset, the proposed method has a total accuracy of 92% compared to current machine learning and deep learning models. The performance metrics of precision, recall, F1-score, confusion matrix, and ROC-AUC analysis show stable classification for four affective classes. The findings validate that the fuzzy-deep hybrid model not only enhances prediction accuracy but also enhances interpretability and robustness against noisy physiological signals, making it appropriate for application in healthcare monitoring, adaptive learning, and human–computer interaction.