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 52
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. Our model achieves up to 16.6%
top-1 zero-shot accuracy on unseen subjects, comparable to decoding
performances using individual sleep data. Furthermore, fine-tuning USD on test
subjects enhances decoding accuracy to 25.9% top-1 accuracy, a substantial
improvement over the baseline chance of 6.7%. Model comparison 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) the
pretrain-finetune paradigm to incorporate different subjects, significantly
contribute to these performances. Collectively, our findings and methodologies
represent a significant advancement in the field of sleep decoding.

通过收集全面的电脑图谱（EEG）数据集并设计了 Universal Sleep Decoder（USD）模型，成功地在清醒状态和睡眠状态之间建立了神经表征，实现了对记忆重播的解码，提高了解码准确度。

通用睡眠解码器：跨个体对齐清醒和睡眠神经表达

Universal Sleep Decoder: Aligning awake and sleep neural representation  across subjects

Identifying sleep stages and patterns is an essential part of diagnosing and
treating sleep disorders. With the advancement of smart technologies, sensor
data related to sleeping patterns can be captured easily. In this paper, we
propose a Convolution Neural Network (CNN) architecture that improves the
classification performance. In particular, we benchmark the classification
performance from different methods, including traditional machine learning
methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest
Neighbour (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM), on 3
publicly available sleep datasets. The accuracy, sensitivity, specificity,
precision, recall, and F-score are reported and will serve as a baseline to
simulate the research in this direction in the future.

利用卷积神经网络结构对现代智能科技中可捕获的人类睡眠数据进行分类，提高睡眠阶段识别精度，并在三个公开数据集上与传统机器学习方法如逻辑回归、决策树、K 最近邻、朴素贝叶斯和支持向量机进行基准测试的性能表现进行对比，以此为未来研究模拟奠定基础。