  In recent years, emotion recognition based on electroencephalography (EEG)
has received growing interests in the brain-computer interaction (BCI) field.
The neuroscience researches indicate that the left and right brain hemispheres
demonstrate activity differences under different emotional activities, which
could be an important principle for designing deep learning (DL) model for
emotion recognition. Besides, owing to the nonstationarity of EEG signals,
using convolution kernels of a single size may not sufficiently extract the
abundant features for EEG classification tasks. Based on these two angles, we
proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM)
based on convolutional neural network (CNN) structure. Evaluated on the public
DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class
classification of low-level and high-level states in each of four emotional
dimensions, i.e., arousal, valence, dominance and liking, respectively. This
study further demonstrated the promising potential to design the DL model from
the multi-scale characteristics of the EEG data and the neural mechanisms of
the emotion cognition.
