Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

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...

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Vydané v:Frontiers in neurorobotics Ročník 14; s. 10
Hlavní autori: Zafar, Amad, Hong, Keum-Shik
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Research Foundation 18.02.2020
Frontiers Media S.A
Predmet:
Algorithms
brain-machine interface (BMI)
Classification
Cortex (motor)
EEG
Electroencephalography
functional near-infrared spectroscopy (fNIRS)
hemodynamic response
Hemoglobin
I.R. radiation
Infrared spectroscopy
Medical imaging
Motor task performance
Neuroscience
Physiology
prediction
Spectrum analysis
tracking
vector phase analysis
prediction
brain-machine interface (BMI)
functional near-infrared spectroscopy (fNIRS)
hemodynamic response
tracking
vector phase analysis
ISSN:1662-5218, 1662-5218
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Shrnutí: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 = 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.
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Edited by: Ganesh R. Naik, Western Sydney University, Australia
Reviewed by: Michele Luigi Pierro, Vivonics, United States; Uma Shahani, Glasgow Caledonian University, United Kingdom
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2020.00010