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English(EN) StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation

StableMind 通过正则化自适应框架改进功能磁共振成像解码

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 StableMind，一个用于解码功能磁共振成像 (fMRI) 数据的新框架。该方法通过提高大脑表征的稳定性和图像监督的可靠性，解决了在数据有限的情况下将模型适应新主体所面临的挑战。StableMind 利用大脑数据的自适应先验和基于傅立叶的增强，以及用于对齐的难度感知图像模糊，在图像和大脑检索任务中取得了更高的准确性。 AI

影响 推动了跨主体功能磁共振成像解码，可能改进脑机接口和神经科学研究。

排序理由 这是一篇详细介绍功能磁共振成像解码新框架的研究论文。

在 arXiv cs.CV 阅读 →

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StableMind 通过正则化自适应框架改进功能磁共振成像解码

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jintao Guo, Lin Wang, Shumeng Li, Jian Zhang, Yulin Zhou, Luyang Cao, Hairong Zheng, Yinghuan Shi ·

    StableMind：无需源数据的跨主体fMRI解码与正则化自适应

    arXiv:2605.02586v1 Announce Type: new Abstract: Existing cross-subject fMRI decoding methods typically train a model on multiple scanned subjects and then adapt it to a new subject using substantial paired fMRI-image data. However, in realistic scenarios, new-subject fMRI data ar…

  2. arXiv cs.CV TIER_1 English(EN) · Yinghuan Shi ·

    StableMind：无需源数据的正则化自适应跨主体fMRI解码

    Existing cross-subject fMRI decoding methods typically train a model on multiple scanned subjects and then adapt it to a new subject using substantial paired fMRI-image data. However, in realistic scenarios, new-subject fMRI data are often limited due to costly data acquisition, …

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