Predicting Human Emotions using EEG-based Brain computer Interface and Interpretable Machine Learning

EEG-based brain-computer interface (BCI) devices have proved to be powerful tools for predicting human emotions. Although Deep learning (DL) techniques have been extensively used to build emotion recognition architectures using EEG-based BCI, they lack interpretability. We propose a prototype of an EEG-based emotion recognition system that can detect the user's emotional state using a deep learning model embedded into an interpretable framework to analyze the decisions of the model and the contributions of the features. The proposed model achieves high performance while showing relevant information on the impact of frequency and spatial features used to predict the emotional states.