Electroencephalogram based face emotion recognition using multimodal fusion and 1-D convolution neural network (ID-CNN) classifier

Abstract

Recently, there has been increased interest in emotion recognition. It is widely utilised in many industries, including healthcare, education and human-computer interaction (HCI). Different emotions are frequently recognised using characteristics of human emotion. Multimodal emotion identification based on the fusion of several features is currently the subject of increasing amounts of research. In order to obtain a superior classification performance, this work offers a deep learning model for multimodal emotion identification based on the fusion of electroencephalogram (EEG) signals and facial expressions. First, the face features from the facial expressions are extracted using a pre-trained convolution neural network (CNN). In this article, we employ CNNs to acquire spatial features from the original EEG signals. These CNNs use both regional and global convolution kernels to learn the characteristics of the left and right hemisphere channels as well as all EEG channels. Exponential canonical correlation analysis (ECCA) is used to combine highly correlated data from facial video frames and EEG after extraction. The 1-D CNN classifier uses these combined features to identify emotions. In order to assess the effectiveness of the suggested model, this research ran tests on the DEAP dataset. It is found that Multi_Modal_1D-CNN achieves 98.9% of accuracy, 93.2% of precision, 89.3% of recall, 94.23% of F1-score and 7sec of processing time.

Keywords

• electroencephalogram (eeg)
• emotion recognition
• cnn
• feature fusion
• pre-processing
• canonical correlation