International Journal of Neural Systems
- Fecha: 01 enero 2018
- ISSN: 17936462 01290657
- Tipo de fuente: Revista
- DOI: 10.1142/S0129065718500442
- Tipo de documento: Artículo en prensa
- Editorial: World Scientific Publishing Co. Pte Ltd email@example.com
© 2018 World Scientific Publishing Company. The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1s to 12s segments, was 12s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification.