EEG-Based Fear Detection in Women

Emotion classification and assessment could be a potential tool to enhance women's protection in gender-based violence situations through fear-recognition systems. Despite advances in this field, female-focused studies of emotion recognition are lacking, even if there is evidence of socialisation-related gender differences in emotion recognition and expression. To create a women-oriented EEG database focused on fear, this research explores different preprocessing, feature extraction, and selection methods, along with machine learning classifiers, to establish an effective fear recognition pipeline. The findings suggest that using a Random Forest with a 4 s window size, features selected using the MRMR algorithm, self-reported labels, and incorporating the gamma frequency band yield the optimal pipeline. To validate said results, the pipeline is tested with strict data splits (LOSO, LASO, and LOVO) as well as with a public database (SEED IV). Overall, these insights contribute to the development of EEG-based fear recognition systems specifically tailored to women's protection.