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

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.

Automated summary

In recent years, there has been a growing interest in emotion recognition based on electroencephalography (EEG) in the brain-computer interaction (BCI) field. This study proposed a Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on a convolutional neural network (CNN) structure, taking into account both the activity differences in the left and right brain hemispheres and the nonstationarity of EEG signals. The model achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, demonstrating its potential for designing deep learning models for emotion recognition.