scholarly journals Optimal quantitation of the cerebral hemodynamic response in functional near-infrared spectroscopy

2010 ◽  
Vol 18 (18) ◽  
pp. 19386 ◽  
Author(s):  
Irina Schelkanova ◽  
Vladislav Toronov
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hemodynamic Response ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Hemodynamic

Relationship Between Age and Cerebral Hemodynamic Response to Breath Holding: A Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 34 (2) ◽  
pp. 154-166
Author(s):  
Keerthana Deepti Karunakaran ◽  
Katherine Ji ◽  
Donna Y. Chen ◽  
Nancy D. Chiaravalloti ◽  
Haijing Niu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hemodynamic Response ◽  
Breath Holding ◽  
Spectroscopy Study ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Hemodynamic

scholarly journals Evoked Hemodynamic Response Estimation to Auditory Stimulus Using Recursive Least Squares Adaptive Filtering with Multidistance Measurement of Near-Infrared Spectroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Zhang ◽  
Xin Liu ◽  
Dan Liu ◽  
Chunling Yang ◽  
Qisong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Auditory Stimulus ◽  
Near Infrared Spectroscopy ◽  
Adaptive Filtering ◽  
Near Infrared ◽  
Continuous Wave ◽  
Hemodynamic Response ◽  
Recursive Least Squares ◽  
Functional Near Infrared Spectroscopy

The performance of functional near-infrared spectroscopy (fNIRS) is sometimes degraded by the interference caused by the physical or the systemic physiological activities. Several interferences presented during fNIRS recordings are mainly induced by cardiac pulse, breathing, and spontaneous physiological low-frequency oscillations. In previous work, we introduced a multidistance measurement to reduce physiological interference based on recursive least squares (RLS) adaptive filtering. Monte Carlo simulations have been implemented to evaluate the performance of RLS adaptive filtering. However, its suitability and performance on human data still remain to be evaluated. Here, we address the issue of how to detect evoked hemodynamic response to auditory stimulus using RLS adaptive filtering method. A multidistance probe based on continuous wave fNIRS is devised to achieve the fNIRS measurement and further study the brain functional activation. This study verifies our previous findings that RLS adaptive filtering is an effective method to suppress global interference and also provides a practical way for real-time detecting brain activity based on multidistance measurement.

scholarly journals Cerebral Hemodynamic Response to Unpleasant Odors in the Preterm Newborn Measured by Near-Infrared Spectroscopy

2001 ◽  
Vol 50 (3) ◽  
pp. 324-330 ◽  
Author(s):  
Marco Bartocci ◽  
Jan Winberg ◽  
Gesa Papendieck ◽  
Teresa Mustica ◽  
Giovanni Serra ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hemodynamic Response ◽  
Preterm Newborn ◽  
Cerebral Hemodynamic

scholarly journals Multimodal Functional Near-Infrared Spectroscopy in Monitoring Cerebral Haemodynamic: A Review Article

2020 ◽  
Vol 4 (1) ◽  
pp. 47-52
Author(s):  
Fairuz Mohd Nasir ◽  
Hiroshi Watabe
Keyword(s):  
Computed Tomography ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Blood Oxygenation ◽  
Imaging Modalities ◽  
Emission Computed Tomography ◽  
Functional Near Infrared Spectroscopy ◽  
Xenon Ct ◽  
Cerebral Hemodynamic

Functional near-infrared spectroscopy (fNIRS) is an optical imaging tool to study brain activities. Moreover, many researchers combined fNIRS with other modalities to gain a better understanding of the brain. This paper provides an overview of the combination of fNIRS with other imaging modalities in the detection and measurement of the cerebral hemodynamic. Cerebral haemodynamic such as the cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral blood oxygenation (CBO) are the important parameters in many neuroimaging studies. Cerebral hemodynamic had been studied by various medical imaging modalities.  Initially, Xenon enhanced Computed Tomography (Xenon CT), Computed Tomography (CT) perfusion; Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) are used to measure the cerebral hemodynamic. Recently, fNIRS is used to optically observe the changes in cerebral haemodynamic during brain activities and the combination of fNIRS with other modalities also become an interest to study the relations within brain activities and the cerebral hemodynamic. Therefore, this paper provides an overview of existing multimodal fNIRS in detection of cerebral haemodynamic changes and provides an important insight on how multimodal fNIRS aid in advancing modern investigations of human brain function.       Keywords: multimodal imaging, fNIRS-fMRI, fNIRS-PET, fNIRS-EEG

scholarly journals Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Kunqiang Qing ◽  
Ruisen Huang ◽  
Keum-Shik Hong
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Classification Performance ◽  
Hemodynamic Responses ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Hemodynamic

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model

2018 ◽  
Vol 26 (2) ◽  
pp. 79-86 ◽  
Author(s):  
Gihyoun Lee ◽  
Seung Hyun Lee ◽  
Sang Hyeon Jin ◽  
Jinung An
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
General Linear Model ◽  
Hemodynamic Response ◽  
Response Model ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Wavelet Shrinkage ◽  
Functional Near Infrared Spectroscopy

Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subject's movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms.

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