Differential Entropy Preserves Variational Information of Near-Infrared Spectroscopy Time Series Associated With Working Memory

• Published in: Frontiers in neuroinformatics Vol. 12; p. 33
• Main Authors: Keshmiri, Soheil, Sumioka, Hidenubo, Yamazaki, Ryuji, Ishiguro, Hiroshi
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 05.06.2018; Frontiers Media S.A
• Subjects: Aging; brain activity decoding; Brain mapping; Brain research; Comparative analysis; differential entropy; Entropy; Functional magnetic resonance imaging; Hemoglobin; Hypothesis testing; I.R. radiation; Infrared spectroscopy; Memory; near-infrared spectroscopy; Nervous system; Neuroimaging; Neuroscience; NIRS time series feature extraction; Short term memory; Spectrum analysis; Statistical analysis; Time series; working memory; Japan; differential entropy; near-infrared spectroscopy; brain activity decoding; working memory; NIRS time series feature extraction
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2018.00033

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.