TY - JOUR AU - Guo, Weili AU - Li, Guangyu AU - Lu, Jianfeng AU - Yang, Jian PY - 2021 M3 - Original Research TI - Singular Learning of Deep Multilayer Perceptrons for EEG-Based Emotion Recognition JO - Frontiers in Computer Science UR - https://www.frontiersin.org/articles/10.3389/fcomp.2021.786964 VL - 3 SN - 2624-9898 N2 - Human emotion recognition is an important issue in human–computer interactions, and electroencephalograph (EEG) has been widely applied to emotion recognition due to its high reliability. In recent years, methods based on deep learning technology have reached the state-of-the-art performance in EEG-based emotion recognition. However, there exist singularities in the parameter space of deep neural networks, which may dramatically slow down the training process. It is very worthy to investigate the specific influence of singularities when applying deep neural networks to EEG-based emotion recognition. In this paper, we mainly focus on this problem, and analyze the singular learning dynamics of deep multilayer perceptrons theoretically and numerically. The results can help us to design better algorithms to overcome the serious influence of singularities in deep neural networks for EEG-based emotion recognition. ER -