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Self-supervised learning has been gaining attention in the field of sleep stage classification. It learns representations with unlabeled electroencephalography (EEG) signals, which alleviates the cost of labeling for specialists. However, most self-supervised approaches assume only the two augmented views from the same EEG sample is a positive pair, which suffers from the false negative problem. Therefore, we propose a new model named Temporal Augmentation and False Negative Suppression (TA-FNS) to solve the problem. Specifically, it first generates two augmented views for each EEG sample.