A Fuzzy Ensemble-Based Deep learning Model for EEG-Based Emotion Recognition

Emotion recognition from EEG signals is a major field of research in cognitive computing. The major challenges involved in the task are extracting meaningful features from the signals and building an accurate model. This paper proposes a fuzzy ensemble-based deep learning approach to classify emotions from EEG-based models. Three individual deep learning models have been trained and combined using a fuzzy rank-based approach implemented using the Gompertz function. The model has been tested on two benchmark datasets: DEAP and AMIGOS. Our model has achieved 90.84% and 91.65% accuracies on the valence and arousal dimensions, respectively, for the DEAP dataset. The model also achieved accuracy above 95% on the DEAP dataset for the subject-dependent approach. On the AMIGOS dataset, our model has achieved state-of-the-art accuracies of 98.73% and 98.39% on the valence and arousal dimensions, respectively. The model achieved accuracies of 99.38% and 98.66% for the subject-independent and subject-dependent cases, respectively. The proposed model has provided satisfactory results on both DEAP and AMIGOS datasets and in both subject-dependent and subject-independent setups. Hence, we can conclude that this is a robust model for emotion recognition from EEG signals.