Title: MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals. Abstract: Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Meanwhile, rapidly developing deep learning methods offer a wide range of opportunities for better modeling brain signals, which has attracted considerable research efforts recently. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, in this paper, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain. Inspired by this, we propose to learn implicit spatial and temporal correlations between different channels (i.e., contacts of the electrode, corresponding to different brain areas) as the cornerstone for uniformly modeling different types of brain signals. Specifically, we capture the temporal correlation by designing the delayed-time-shift prediction task; we represent the spatial correlation by a graph structure, which is built with the goal to maximize the mutual information of each channel and its correlated ones. We further theoretically prove that our design can lead to a better predictive representation. Extensive experiments of seizure detection on both EEG and SEEG large-scale real- world datasets demonstrate our model outperforms several state-of-the-art time series SSL and unsupervised models.