EEG SIGNAL-DRIVEN HUMAN–COMPUTER INTERACTION EMOTION RECOGNITION MODEL USING AN ATTENTIONAL NEURAL NETWORK ALGORITHM - CHANG, WEI; XU, LIJUN; YANG, QING; MA, YIMING (Journal of mechanics in medicine and biology, 2023)
The level of human–machine interaction experience is raising its bar as artificial intelligence develops quickly. An important trend in this application is the improvement of the friendliness, harmony, and simplicity of human–machine communication. Electroencephalogram (EEG) signal-driven emotion identification has recently gained popularity in the area of human–computer interaction (HCI) because of its advantages of being simple to extract, difficult to conceal, and real-time differences. The corresponding research is ultimately aimed at imbuing computers with feelings to enable fully harmonic and organic human–computer connections. This study applies three-dimensional convolutional neural networks (3DCNNs) and attention mechanisms to an environment for HCI and offers a dual-attention 3D convolutional neural networks (DA-3DCNNs) model from the standpoint of spatio-temporal convolution. With the purpose of extracting more representative spatio-temporal characteristics, the new model first thoroughly mines the spatio-temporal distribution information of EEG signals using 3DCNN, taking into account the temporal fluctuation of EEG data. Yet, a dual-attention technique based on EEG channels is utilized at the same time to strengthen or weaken the feature information and understand the links between various brain regions and emotional activities, highlighting the variations in the spatiotemporal aspects of various emotions. Finally, three sets of experiments were planned on the Database for Emotion Analysis using Physiological Signals (DEAP) dataset for cross-subject emotion classification experiments, channel selection experiments, and ablation experiments, respectively, to show the validity and viability of the DA-3DCNN model for HCI emotion recognition applications. The outcomes show that the new model may significantly increase the model’s accuracy in recognizing emotions, acquire the spatial relationship of channels, and more thoroughly extract dynamic information from EEG.