Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel relations that are known to be beneficial for EEG-based classification. Additionally, these models require substantial data for training, which is often impractical due to the high cost of data collection. To simultaneously address these two issues, we propose a Self-Attentive Channel-Connectivity Capsule Network (SACC-CapsNet) for EEG-based driving fatigue detection in this paper. SACC-CapsNet starts with a temporal-channel attention module to investigate the critical temporal information and important channels for driving fatigue detection, refining the input EEG signals. Subsequently, the refined EEG data are transformed into a channel covariance matrix to capture the inter-channel relations, followed by selective kernel attention to extract the highly discriminative channel-connectivity features. Finally, a capsule neural network is employed to effectively learn the relationships between connectivity features, which is more suitable for limited data. To confirm the effectiveness of SACC-CapsNet, we collected 24-channel EEG data from 31 subjects (mean age=23.13±2.68 years, male/female=18/13) in a simulated fatigue driving environment. Extensive experiments were conducted with the acquired data, and the comparison results show that our proposed model outperforms state-of-the-art methods. Additionally, the channel covariance matrix learned from SACC-CapsNet reveals that the frontal pole is most informative for detecting driving fatigue, followed by the parietal and central regions. Intriguingly, the temporal-channel attention module can enhance the significance of these critical regions, and the reconstructed channel covariance matrix generated by the decoder network of SACC-CapsNet can effectively preserve valuable information about them.