  Emotion recognition based on EEG has become an active research area. As one
of the machine learning models, CNN has been utilized to solve diverse problems
including issues in this domain. In this work, a study of CNN and its
spatiotemporal feature extraction has been conducted in order to explore
capabilities of the model in varied window sizes and electrode orders. Our
investigation was conducted in subject-independent fashion. Results have shown
that temporal information in distinct window sizes significantly affects
recognition performance in both 10-fold and leave-one-subject-out cross
validation. Spatial information from varying electrode order has modicum effect
on classification. SVM classifier depending on spatiotemporal knowledge on the
same dataset was previously employed and compared to these empirical results.
Even though CNN and SVM have a homologous trend in window size effect, CNN
outperformed SVM using leave-one-subject-out cross validation. This could be
caused by different extracted features in the elicitation process.
