  Understanding semantic content from brain activity during sleep represents a
major goal in neuroscience. While studies in rodents have shown spontaneous
neural reactivation of memories during sleep, capturing the semantic content of
human sleep poses a significant challenge due to the absence of well-annotated
sleep datasets and the substantial differences in neural patterns between
wakefulness and sleep. To address these challenges, we designed a novel
cognitive neuroscience experiment and collected a comprehensive, well-annotated
electroencephalography (EEG) dataset from 134 subjects during both wakefulness
and sleep. Leveraging this benchmark dataset, we developed SI-SD that enhances
sleep semantic decoding through the position-wise alignment of neural latent
sequence between wakefulness and sleep. In the 15-way classification task, our
model achieves 24.12% and 21.39% top-1 accuracy on unseen subjects for NREM 2/3
and REM sleep, respectively, surpassing all other baselines. With additional
fine-tuning, decoding performance improves to 30.32% and 31.65%, respectively.
Besides, inspired by previous neuroscientific findings, we systematically
analyze how the "Slow Oscillation" event impacts decoding performance in NREM
2/3 sleep -- decoding performance on unseen subjects further improves to
40.02%. Together, our findings and methodologies contribute to a promising
neuro-AI framework for decoding brain activity during sleep.
