reduction of onset delay in functional near-infrared spectroscopy: prediction of hbo/hbr signals

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ID: 134896
2020
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Abstract
An intrinsic problem when using hemodynamic responses for the brain-machine interface is the slow nature of the physiological process. In this paper, a novel method that estimates the oxyhemoglobin changes caused by neuronal activations is proposed and validated. In monitoring the time responses of blood-oxygen-level-dependent signals with functional near-infrared spectroscopy (fNIRS), the early trajectories of both oxy- and deoxy-hemoglobins in their phase space are scrutinized. Furthermore, to reduce the detection time, a prediction method based upon a kernel-based recursive least squares (KRLS) algorithm is implemented. In validating the proposed approach, the fNIRS signals of finger tapping tasks measured from the left motor cortex are examined. The results show that the KRLS algorithm using the Gaussian kernel yields the best fitting for both ΔHbO (i.e., 87.5%) and ΔHbR (i.e., 85.2%) at q = 15 steps ahead (i.e., 1.63 s ahead at a sampling frequency of 9.19 Hz). This concludes that a neuronal activation can be concluded in about 0.1 s with fNIRS using prediction, which enables an almost real-time practice if combined with EEG.
Reference Key
zafar2020frontiersreduction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Amad Zafar;Amad Zafar;Keum-Shik Hong;Keum-Shik Hong
Journal industrial \& engineering chemistry research
Year 2020
DOI
10.3389/fnbot.2020.00010
URL
https://www.frontiersin.org/article/10.3389/fnbot.2020.00010/full
Keywords
prediction tracking neuropsychiatry biological psychiatry

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