Affective brain–computer interfaces have become an increasingly important topic to achieve emotional intelligence in human–machine collaboration. However, due to the complexity of signals and the individual differences in emotional response, it is still a great challenge to design a reliable and effective model. Considering the influence of on emotional response, it would be helpful to integrate personality information and EEG signals for . This study proposes a personality-guided attention neural network that can use personality information to learn effective EEG representations for . Specifically, we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals, and a special convolution kernel is designed to learn inter- and intra-regional correlations simultaneously. Second, inspired by the fact that electrodes within distinct brain scalp regions play different roles in , a personality-guided regional- is proposed to further explore the contributions of electrodes within a region and between regions. Finally, attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals. Experiments on the AMIGOS dataset, which is a dataset for multimodal research for affect, , and mood on individuals and groups, show that the proposed method can significantly improve the performance of subject-independent and outperform state-of-the-art methods.