Differential entropy preserves variational information of near-infrared spectroscopy time series associated with working memory

Soheil Keshmiri*, Hidenubo Sumioka, Ryuji Yamazaki Skov, Hiroshi Ishiguro

*この研究の対応する著者

研究成果: Article査読

10 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号33
ジャーナルFrontiers in Neuroinformatics
12
DOI
出版ステータスPublished - 2018 6 5

ASJC Scopus subject areas

  • 神経科学(その他)
  • 生体医工学
  • コンピュータ サイエンスの応用

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