  Emotion recognition using EEG signals is an emerging area of research due to
its broad applicability in BCI. Emotional feelings are hard to stimulate in the
lab. Emotions do not last long, yet they need enough context to be perceived
and felt. However, most EEG-related emotion databases either suffer from
emotionally irrelevant details (due to prolonged duration stimulus) or have
minimal context doubting the feeling of any emotion using the stimulus. We
tried to reduce the impact of this trade-off by designing an experiment in
which participants are free to report their emotional feelings simultaneously
watching the emotional stimulus. We called these reported emotional feelings
"Emotional Events" in our Dataset on Emotion with Naturalistic Stimuli (DENS).
We used EEG signals to classify emotional events on different combinations of
Valence(V) and Arousal(A) dimensions and compared the results with benchmark
datasets of DEAP and SEED. STFT is used for feature extraction and used in the
classification model consisting of CNN-LSTM hybrid layers. We achieved
significantly higher accuracy with our data compared to DEEP and SEED data. We
conclude that having precise information about emotional feelings improves the
classification accuracy compared to long-duration EEG signals which might be
contaminated by mind-wandering.
