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Updated: Jan 11, 2026

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基于变压器的深度学习模型用于预测fNIRS短通道信号.

Sabino Guglielmini1, Vittoria Banchieri1, Felix Scholkmann1,2,3

  • 1University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Department of Neonatology, Zurich, Switzerland.

Neurophotonics
|November 17, 2025
PubMed
概括

这项研究引入了一种深度学习模型,用于预测功能近红外光谱 (fNIRS) 数据中的脑外信号. 这种虚拟方法为有效的血液动力学信号校正提供了一个硬件独立的替代方案.

关键词:
深度学习是一种深度学习.功能近红外光谱学近红外光谱学功能性近红外光谱学预处理生理学噪音是指生理学的噪音.短频道回归的方法信号无声化 信号无声化变压器编码器编码器

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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