脑功能近红外光谱信号多元图特征提取研究

Feature Extraction of Brain Functional Near-Infrared Spectroscopy Signals Based on Multivariate Graph Theory

脑功能近红外光谱(fNIRS)的信号分析和模式识别方法,对其在认知科学领域的研究和应用尤为重要。简述了fNIRS的传统统计特征提取方法,进而提出了基于多元图表示原理进行特征提取的方法,并对传统方法与提出方法的模式识别实验进行了对比研究。实验结果表明基于多元图表示原理的fNIRS信号特征提取方法能应用于信号的分析和可视化,为fNIRS信号的数据分析提供了新的方法。

Signal analysis and pattern recognition methods for brain functional near-infrared spectroscopy （fNIRS） are especially important for its research and applications in the field of cognitive science. The traditional statistical feature extraction method for fNIRS is briefly reviewed and a new feature extraction method based on the principle of multivariate graph representation is proposed. The pattern recognition experiments based on both methods are conducted and compared. The experimental results indicate that the feature extraction method for fNIRS signals based on the multivariate graph representation principle can be used for signal analysis and visualization, which offers a new approach for the analysis of fNIRS signals.