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Updated: Jan 9, 2026

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基于格拉米安角差异场和一个时空特征融合网络的功能近红外光谱学的分类

Yiling Wen, Yuetong An, Mengxiang Chu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

本研究介绍了VisiTempNet,这是一个用于功能近红外光谱 (fNIRS) 数据的深度学习模型. 它有效地结合了时间序列和图像特征,提高了脑计算机接口 (BCI) 的准确性.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
机器学习机器学习

背景情况:

功能近红外光谱 (fNIRS) 是一种非侵入性神经成像技术,用于脑电脑界面 (BCI) 研究和临床环境.
从1D fNIRS时间序列数据中提取复杂的模式是一个重大挑战.
格拉米安角差值场 (GADF) 将时间序列转换为2D图像以增强特征表示,但其与时间序列特征的组合尚未被探索.

研究的目的:

提出VisiTempNet,一个用于fNIRS数据分析的新型深度学习模型.
使用时空融合方法将时间序列和GADF图像特征集成.
为了提高fNIRS应用中信号分类的准确性.

主要方法:

开发了VisiTempNet,这是一个结合时空融合的深度学习模型.
应用卷积到时间序列数据,专注于延迟的血液动力学反应.
使用并行模块进行特征提取,然后进行时间序列和GADF图像特征的规范化和加权融合.

主要成果:

在fNIRS2MW数据集上,VisiTempNet实现了76.65±2.43%的分类准确度.
拟议的模型在实验评估中表现优于所有基线模型.
证明了将GADF图像特征与传统时间序列特征相结合的有效性.

结论:

整合GADF图像特征和时间序列数据显著增强了fNIRS信号分类.
VisiTempNet代表了分析fNIRS数据中复杂模式的卓越方法.
这些发现验证了该模型在推进BCI和临床应用方面的潜力.

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