EEMD-ICA Applied in Signal Extraction in Functional Near-Infrared Spectroscopy
ZHA Yu-tong1, LIU Guang-da1*, ZHOU Run-dong1, ZHANG Xiao-feng1, NIU Jun-qi2, YU Yong1, WANG Wei1
1. College of Instrument Science and Electrical Engineering, Jilin University, Changchun 130061, China 2. First Hospital, Jilin University, Changchun 130021, China
Abstract：Currently, functional near-infrared spectroscopy (fNIRS) is widely used in the field of Neuroimaging. To solve the signal-noise frequency spectrum aliasing in non-linear and non-stationary fNIRS characteristic signal extraction, a new joint multi-resolution algorithm, EEMD-ICA, is proposed based on combining Independent Component Analysis with Ensemble Empirical Mode Decomposing. After functional brain imaging instrument detected the multi-channel and multi-wavelength NIR optical density signals, EEMD was performed to decompose measurement signals into multiple intrinsic mode function according to the signal frequency component. Then ICA was applied to extract the interest data from IMFs into ICs. Finally, reconstructed signals were obtained by accumulating the ICs set. EEMD-ICA was applied in de-noising Valsalva test signals which were considered as original signals and compared with Empirical Mode Decomposing and Ensemble Empirical Mode Decomposing to illustrate validity of this algorithm. It is proved that useful information loss during de-noising and invalidity of noise elimination are completely solved by EEMD-ICA. This algorithm is more optimized than other two de-noising methods in error parameters and signal-noise-ratio analysis.
[1] Jobsis F F, et al. Science, 1977, 198: 1264. [2] Hoshi Y, Tamura M. Neurosci. Lett., 1993, 150(1): 5. [3] Hoshi Y, Tamura M. J. Appl. Physiol., 1993, 75(4): 1842. [4] YAN Shi-yu, WANG Hong, ZHAO Hai-bin, et al(颜世玉, 王 宏，赵海滨，等). Journal of Northeastern University·Natural Science(东北大学学报·自然科学版), 2012, 33(10): 1504. [5] Huang N E，Shen Z，Long S R, et al. Proc. R. Soc. Lond, 1998, 454: 903. [6] Boudraa A O, Cexus J C. IEEE Transactions on Instrumentation and Measurement, 2007, 56(6): 2196. [7] ZHOU Zhen-yu, YANG Hong-yu, GONG Hui, et al(周振宇, 杨宏宇，龚 辉，等). Acta Optica Sinica(光学学报)，2007, 27(2): 307. [8] Wu Zhaohua，Huang Norden E. Advances in Adaptive Data Analysis, 2009，1(1)：1. [9] GONG Yu-wen, ZHANG Gui-yun, MA Hong-zhi(公昱文, 张桂芸, 马洪芝). Computer Engineering & Science(计算机工程与科学), 2008, 30(10): 37. [10] Hyvrinen A，Oja E. Neural Networks, 2000, 13(4-5): 411. [11] GU Jun, HU Xian-dan(顾 军, 胡显丹). ShipBoard Electronic Countermeature，2009, 32(5)：41. [12] Henderson A, Woo M A, Macey P M, et al. J. Appl. Physiol., 2003, 94: 1063.
