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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 hemispheresdemonstrate activity differences under different emotional activities, whichcould be an important principle for designing deep learning (DL) model foremotion recognition. Besides, owing to the nonstationarity of EEG signals,using convolution kernels of a single size may not sufficiently extract theabundant features for EEG classification tasks. Based on these two angles, weproposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM)based on convolutional neural network (CNN) structure. Evaluated on the publicDEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-classclassification of low-level and high-level states in each of four emotionaldimensions, i.e., arousal, valence, dominance and liking, respectively. Thisstudy further demonstrated the promising potential to design the DL model fromthe multi-scale characteristics of the EEG data and the neural mechanisms ofthe emotion cognition.

Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition