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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| Online Access: | https://arxiv.org/abs/2309.16457 |
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| _version_ | 1866917669363515392 |
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| author | Zheng, Hui Chen, Zhong-Tao Wang, Hai-Teng Zhou, Jian-Yang Zheng, Lin Lin, Pei-Yang Liu, Yun-Zhe |
| author_facet | Zheng, Hui Chen, Zhong-Tao Wang, Hai-Teng Zhou, Jian-Yang Zheng, Lin Lin, Pei-Yang Liu, Yun-Zhe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16457 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SI-SD: Sleep Interpreter through awake-guided cross-subject Semantic Decoding Zheng, Hui Chen, Zhong-Tao Wang, Hai-Teng Zhou, Jian-Yang Zheng, Lin Lin, Pei-Yang Liu, Yun-Zhe Machine Learning Signal Processing Neurons and Cognition 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. |
| title | SI-SD: Sleep Interpreter through awake-guided cross-subject Semantic Decoding |
| topic | Machine Learning Signal Processing Neurons and Cognition |
| url | https://arxiv.org/abs/2309.16457 |