  Inter-subject or subject-independent emotion recognition has been a
challenging task in affective computing. This work is about an
easy-to-implement emotion recognition model that classifies emotions from EEG
signals subject independently. It is based on the famous EEGNet architecture,
which is used in EEG-related BCIs. We used the Dataset on Emotion using
Naturalistic Stimuli (DENS) dataset. The dataset contains the Emotional Events
-- the precise information of the emotion timings that participants felt. The
model is a combination of regular, depthwise and separable convolution layers
of CNN to classify the emotions. The model has the capacity to learn the
spatial features of the EEG channels and the temporal features of the EEG
signals variability with time. The model is evaluated for the valence space
ratings. The model achieved an accuracy of 73.04%.
