Keshmiri Soheil, Sumioka Hidenubo, Yamazaki Ryuji, Ishiguro Hiroshi
Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan.
School of Social Sciences, Waseda University, Tokyo, Japan.
Front Neuroinform. 2018 Jun 5;12:33. doi: 10.3389/fninf.2018.00033. eCollection 2018.
Neuroscience research shows a growing interest in the application of Near-Infrared Spectroscopy (NIRS) in analysis and decoding of the brain activity of human subjects. Given the correlation that is observed between the Blood Oxygen Dependent Level (BOLD) responses that are exhibited by the time series data of functional Magnetic Resonance Imaging (fMRI) and the hemoglobin oxy/deoxy-genation that is captured by NIRS, linear models play a central role in these applications. This, in turn, results in adaptation of the feature extraction strategies that are well-suited for discretization of data that exhibit a high degree of linearity, namely, slope and the mean as well as their combination, to summarize the informational contents of the NIRS time series. In this article, we demonstrate that these features are inefficient in capturing the variational information of NIRS data, limiting the reliability and the adequacy of the conclusion on their results. Alternatively, we propose the linear estimate of differential entropy of these time series as a natural representation of such information. We provide evidence for our claim through comparative analysis of the application of these features on NIRS data pertinent to several working memory tasks as well as naturalistic conversational stimuli.
神经科学研究显示，人们对近红外光谱技术（NIRS）在分析和解码人类受试者大脑活动方面的应用兴趣日益浓厚。鉴于功能磁共振成像（fMRI）的时间序列数据所呈现的血氧依赖水平（BOLD）反应与NIRS所捕捉的血红蛋白氧合/脱氧之间存在的相关性，线性模型在这些应用中发挥着核心作用。这进而导致适用于对具有高度线性的数据（即斜率、均值及其组合）进行离散化的特征提取策略被采用，以总结NIRS时间序列的信息内容。在本文中，我们证明这些特征在捕捉NIRS数据的变化信息方面效率低下，限制了基于其结果得出结论的可靠性和充分性。相反，我们提出将这些时间序列的微分熵的线性估计作为此类信息的自然表示。我们通过对这些特征在与多个工作记忆任务以及自然对话刺激相关的NIRS数据上的应用进行比较分析，为我们的主张提供了证据。
