论文标题
使用功能近红外光谱法检测认知负荷的血流动力分解模型
A hemodynamic decomposition model for detecting cognitive load using functional near-infrared spectroscopy
论文作者
论文摘要
在当前的论文中，我们引入了一个用于功能近红外光谱的参数数据驱动模型，该模型将信号分解为一系列独立，重新缩短，时移，血液动力学基础函数。每个分解的波形都保留有关预期血液动力学行为的相关生物学信息。该模型还与有效的迭代估计方法一起提出，以提高计算速度。我们的血液动力分解模型（HDM）扩展了a）外部刺激未知的实例的规范模型，或者b）当实验刺激与血液动力学反应之间直接关系的假设无法保持。我们还认为，提出的方法可以被用作用于机器学习目的的特征转换方法。通过将我们设计的HDM应用于FNIRS信号上的认知负载分类任务，我们使用六个通道在额叶皮层中的六个通道和86.34％+-2.81％的准确度达到了86.20％+-2.56％，并且仅利用AFPZ通道也只有86.34％+-2.81％。相比之下，在相同的实验环境下，最新的时间光谱转换仅产生64.61％+-3.03％和37.8％+-2.96％。
In the current paper, we introduce a parametric data-driven model for functional near-infrared spectroscopy that decomposes a signal into a series of independent, rescaled, time-shifted, hemodynamic basis functions. Each decomposed waveform retains relevant biological information about the expected hemodynamic behavior. The model is also presented along with an efficient iterative estimation method to improve the computational speed. Our hemodynamic decomposition model (HDM) extends the canonical model for instances when a) the external stimuli are unknown, or b) when the assumption of a direct relationship between the experimental stimuli and the hemodynamic responses cannot hold. We also argue that the proposed approach can be potentially adopted as a feature transformation method for machine learning purposes. By virtue of applying our devised HDM to a cognitive load classification task on fNIRS signals, we have achieved an accuracy of 86.20%+-2.56% using six channels in the frontal cortex, and 86.34%+-2.81% utilizing only the AFpz channel also located in the frontal area. In comparison, state-of-the-art time-spectral transformations only yield 64.61%+-3.03% and 37.8%+-2.96% under identical experimental settings.
