近年来, 功能性近红外光谱技术(fNIRS)广泛应用于神经影像学领域。 为解决fNIRS特征信号提取中的信噪频谱混叠问题, 依据近红外光谱脑功能成像信号非线性与非平稳特点, 提出一种结合集合经验模态分解法和独立成分分析的多分辨率联合信号提取方法EEMD-ICA。 在脑功能成像仪器平台上采集多通道多波长脑功能成像近红外光密度信号, 先对该信号进行集合经验模态分解将其按频率成分分解为多层本征模态函数, 之后将独立成分分析应用于目标频率分量函数进行自适应去噪, 最后将处理后的分量累加、 重构获得近红外光谱脑功能成像的特征信号。 将Valsalva氏实验测试数据作为研究对象进行滤噪处理, 与经验模态分解法和集合经验模态分解法对fNIRS特征信号的提取效果对比。 对实测数据的处理结果进行信噪比和误差参数分析, 结果表明, 该方法能够有效解决去噪过程中丢失原始信号有用信息及由于信噪频谱混叠不能完整去除噪声的问题, 信号处理效果理想, 对比另外两种信号提取方法更为优化。

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.