Neuro-Emotional Mapping of Human Emotions via EEG Signals

This study presents a novel approach to emotion recognition using Electroencephalogram (EEG) signals and machine learning, with a focus on deciphering the neural signatures of human emotions. We utilized EmoNeuroDB, a dataset of 720 EEG recordings from 40 participants to explore the efficacy of Random Forest classifiers, optimized through grid search and evaluated via cross-validation, to classify six basic emotions. Our methodology integrates comprehensive signal processing techniques to extract meaningful features from the EEG signals, facilitating the accurate classification of emotional states. The experiment design, involving a sequence of emotion elicitation and relaxation phases, coupled with advanced data analysis, underscores the potential of combining EEG and machine learning for real-time emotion recognition. The findings contribute to the understanding of emotional processing in the brain, offering implications for enhancing human-computer interaction and psychological research.