Universal Sleep Decoder: Aligning awake and sleep neural representation across subjects
CoRR(2023)
摘要
Decoding memory content from brain activity during sleep has long been a goal
in neuroscience. While spontaneous reactivation of memories during sleep in
rodents is known to support memory consolidation and offline learning,
capturing memory replay in humans is challenging 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 the Universal Sleep Decoder (USD) to align neural representations
between wakefulness and sleep across subjects and a real-time staging model
comparable to offline staging algorithms. Our model achieves up to 23.00
21.15
decoding accuracy on unseen subjects for N2/3 stage and REM stage, which is
much higher than the decoding performances using individual sleep data.
Furthermore, fine-tuning USD on test subjects enhances decoding accuracy to
29.20
a substantial improvement over the baseline chance of 6.7
and ablation analyses reveal that our design choices, including the use of (i)
an additional contrastive objective to integrate awake and sleep neural signals
and (ii) a shared encoder to enhance the alignment of awake and sleep neural
signals, significantly contribute to these performances. Collectively, our
findings and methodologies represent a significant advancement in the field of
sleep decoding.
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关键词
universal sleep decoder,neural representation
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