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arXiv · 2309.16457

SI-SD: Sleep Interpreter through awake-guided cross-subject Semantic Decoding

Hui Zheng·Zhong-Tao Chen·Hai-Teng Wang·Jian-Yang Zhou·Lin Zheng·Pei-Yang Lin·Yun-Zhe Liu
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Abstract

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.

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Hui Zheng, Zhong-Tao Chen, Hai-Teng Wang, Jian-Yang Zhou, Lin Zheng, Pei-Yang Lin, Yun-Zhe Liu. 2023-09-28. SI-SD: Sleep Interpreter through awake-guided cross-subject Semantic Decoding. https://arxiv.org/abs/2309.16457

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