从脑影像中解码过去视觉记忆，提升脑机接口精度
Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic Information
- 提出记忆解耦新任务，分离连续脑信号中的当前与过去信息
- 在fMRI数据上实现对过去图像描述的准确解码
- 适合脑机接口、神经科学领域研究者关注
人类视觉系统能处理连续的视觉信息，但大脑在持续视觉刺激下如何编码和提取近期视觉记忆仍不明确。本研究探讨工作记忆在连续视觉刺激下保持过往信息的能力，并提出新的记忆解耦任务，旨在从fMRI信号中提取并解码过去的信息。为缓解过去记忆干扰问题，设计了一种受前摄干扰现象启发的解耦对比学习方法，将相邻fMRI信号中的信息分离为当前与过去成分，并解码为图像描述。实验结果表明，该方法能有效解耦fMRI信号中的信息。本研究可推动脑机接口发展，并缓解fMRI低时间分辨率的问题。
原文摘要 · Abstract (English)
The human visual system is capable of processing continuous streams of visual information, but how the brain encodes and retrieves recent visual memories during continuous visual processing remains unexplored. This study investigates the capacity of working memory to retain past information under continuous visual stimuli. And then we propose a new task Memory Disentangling, which aims to extract and decode past information from fMRI signals. To address the issue of interference from past memory information, we design a disentangled contrastive learning method inspired by the phenomenon of proactive interference. This method separates the information between adjacent fMRI signals into current and past components and decodes them into image descriptions. Experimental results demonstrate that this method effectively disentangles the information within fMRI signals. This research could advance brain-computer interfaces and mitigate the problem of low temporal resolution in fMRI.
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