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In recent years, deep learning (DL) techniques, and in particular convolutional neural networks (CNNs), have shown great potential in electroencephalograph (EEG)-based emotion recognition. However, existing CNN-based EEG emotion recognition methods usually require a relatively complex stage of feature pre-extraction. More importantly, the CNNs cannot well characterize the intrinsic relationship among the different channels of EEG signals, which is essentially a crucial clue for the recognition of emotion. In this paper, we propose an effective multi-level features guided capsule network (MLF-CapsNet) for multi-channel EEG-based emotion recognition to overcome these issues. The MLF-CapsNet is an end-to-end framework, which can simultaneously extract features from the raw EEG signals and determine the emotional states. Compared with original CapsNet, it incorporates multi-level feature maps …