A Novel Dual-Task Model for EEG-Based Emotion and Cognition Recognition

Human cognition and emotion states are interrelated. Constructing electroencephalography (EEG)-based deep learning models to simultaneously recognize the cognitive and emotional states of an individual contributes to cognitive and emotional interaction studies. However, most of the existing models focus on cognition or emotion recognition, neglecting to exploit their shared features in EEG and weakening the generalization performance of the models. In this study, we propose a dual-task learning model DT-EEGNet, where emotion and cognition are considered as related tasks for joint analysis. In DT-EEGNet, EEGNet is employed as a base network responsible for extracting shared features between cognition and emotion recognition tasks. Then, a multiscale ECAWeight attention (MSEA) module is introduced for obtaining key information from these shared features. Finally, a dual-task loss function named dynamic weight average (DWA) is used to balance the training rates of emotion and cognition recognition tasks for better overall training performance. Experimental results on our self-constructed emotion and cognition EEG dataset (ECED) and the public DEAP dataset show that our proposed DT-EEGNet has better dual-task recognition performance. The proposed method can provide a new idea for cognitive impairment assessment research as well.