Kamran M Ahmad, Hong Keum-Shik
Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, Republic of Korea.
Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, Republic of Korea; School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, Republic of Korea.
Neurosci Lett. 2014 Sep 19;580:130-6. doi: 10.1016/j.neulet.2014.07.058. Epub 2014 Aug 8.
This paper presents a methodology for online estimation of brain activities with reduction in the effects of physiological noises in functional near-infrared spectroscopy signals. The input-output characteristics of a hemodynamic response are modeled as an autoregressive moving average model together with exogenous physical signals (i.e., ARMAX). In contrast to the fixed design matrix in the conventional general linear model, the proposed model incorporates the temporal variations in the experimental paradigm as well as in the hemodynamics. The performance of the proposed method has been tested by using box-car type functions followed by individual tapping tasks. The results and their significance were verified using t-statistics indicating that ARMAX seems to be better able to track/reveal the hemodynamic response. Also, online brain-activation maps were generated for localizing brain activities. Experimental results are compared with those of the existing conventional GLM-based method.
本文提出了一种在线估计大脑活动的方法，该方法可减少功能近红外光谱信号中生理噪声的影响。将血液动力学响应的输入输出特性建模为自回归滑动平均模型，并结合外部物理信号（即ARMAX）。与传统通用线性模型中的固定设计矩阵不同，所提出的模型纳入了实验范式以及血液动力学中的时间变化。通过使用方波函数和个体敲击任务来测试所提方法的性能。使用t统计量验证了结果及其显著性，表明ARMAX似乎更能跟踪/揭示血液动力学响应。此外，还生成了在线脑激活图以定位大脑活动。将实验结果与现有的基于传统GLM的方法的结果进行了比较。
