Department of Cogno-Mechatronics Engineering, Pusan National University, San 30 Jangjeon-dong Geumjeong-gu, Busan 609-735, Korea.
J Neural Eng. 2013 Oct;10(5):056002. doi: 10.1088/1741-2560/10/5/056002. Epub 2013 Jul 26.
Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique that measures brain activities by using near-infrared light of 650-950 nm wavelength. The major advantages of fNIRS are its low cost, portability, and good temporal resolution as a plausible solution to real-time imaging. Recent research has shown the great potential of fNIRS as a tool for brain-computer interfaces.
This paper presents the first novel technique for fNIRS-based modelling of brain activities using the linear parameter-varying (LPV) method and adaptive signal processing. The output signal of each channel is assumed to be an output of an LPV system with unknown coefficients that are optimally estimated by the affine projection algorithm. The parameter vector is assumed to be Gaussian.
The general linear model (GLM) is very popular and is a commonly used method for the analysis of functional MRI data, but it has certain limitations in the case of optical signals. The proposed model is more efficient in the sense that it allows the user to define more states. Moreover, unlike most previous models, it is online. The present results, showing improvement, were verified by random finger-tapping tasks in extensive experiments. We used 24 states, which can be reduced or increased depending on the cost of computation and requirements.
The t-statistics were employed to determine the activation maps and to verify the significance of the results. Comparison of the proposed technique and two existing GLM-based algorithms shows an improvement in the estimation of haemodynamic response. Additionally, the convergence of the proposed algorithm is shown by error reduction in consecutive iterations.
功能近红外光谱（fNIRS）是一种新兴的非侵入式脑成像技术，通过使用波长为 650-950nm 的近红外光来测量脑活动。fNIRS 的主要优势在于其低成本、便携性和良好的时间分辨率，是实时成像的可行解决方案。最近的研究表明，fNIRS 作为脑机接口工具具有巨大的潜力。
本文提出了一种基于 LPV 方法和自适应信号处理的 fNIRS 脑活动建模的新技术。假设每个通道的输出信号是具有未知系数的 LPV 系统的输出，这些系数通过仿射投影算法进行最优估计。假设参数向量是高斯的。
广义线性模型（GLM）非常流行，是分析功能磁共振成像数据的常用方法，但在光信号的情况下存在一定的局限性。所提出的模型更有效，因为它允许用户定义更多的状态。此外，与大多数先前的模型不同，它是在线的。在广泛的实验中，通过随机手指敲击任务验证了本研究结果的改进。我们使用了 24 个状态，这些状态可以根据计算成本和要求进行减少或增加。
使用 t 统计量来确定激活图并验证结果的显著性。与两种基于 GLM 的现有算法的比较表明，在估计血液动力学响应方面有所改进。此外，通过连续迭代中的误差减少来显示所提出算法的收敛性。
