Multi-channel Near-Infrared Spectroscopy (NIRS) system for noninvasive monitoring of brain activity

Hemmati, Nima; Setarehdan, Seyed Kamaledin; Noubari, H. Ahmadi

doi:10.1109/bhi.2012.6211548

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…In all experiments, the PFC of subjects was monitored using a 4 channel continuous wave fNIRS system that was developed and evaluated at the University of Tehran [10]. This system has two left and right probes each with one light emitting diode (LED) as NIR sources and two detectors which are located at 1.5 and 3cm away from the source as schematically shown in Fig.…”

Section: B Fnirs Systemmentioning

confidence: 99%

Attention level quantification during a modified stroop color word experiment: an fNIRS based study

Jahani

Berivanlou

Rahimpour

et al. 2015

2015 22nd Iranian Conference on Biomedical Engineering (ICBME)

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Attention is one of the brain's ability to focus on one particular issue among many others. Attention plays a critical role in those tasks that involve detection of rare events such as air traffic control where any attention lapses may cause a disaster. So, measurement and quantification of attention workload levels is critical and can be used to improve one's performance. In this article an effective algorithm for measurement of one's attention in three different levels by means of functional near-infrared spectroscopy is presented. Brain hemodynamic of 8 healthy subjects during a modified stroop color word task over the PFC region is measured using a 4-channel continuous wave functional near infrared spectroscopy (fNIRS) instrument. Since the hemodynamic response of mental activities is contaminated by psychological inferences, the first processing step is extraction of evoked hemodynamic response due to various attention levels. Then, an effective feature of hemodynamic response is extracted and analyzed by ANOVA test. The results of this study demonstrate the feasibility of fNIRS based attention level measurement that could provide valuable information of individuals' ability.

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Section: B Fnirs Systemmentioning

confidence: 99%

Attention level quantification during a modified stroop color word experiment: an fNIRS based study

Jahani

Berivanlou

Rahimpour

et al. 2015

2015 22nd Iranian Conference on Biomedical Engineering (ICBME)

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“…During the stress task, fNIRS signal has been recorded using the fNIRS system designed and developed in the NIR Laboratory of the School of Electrical and Computer Engineering, within the University of Tehran. 35 This system operates with 2 wavelengths of 730 and 850 nm, with a sampling frequency of 3.8 Hz. In this research, a detector has been placed 1.5 cm (near channel) away from the light source and the other detector is placed 3 cm (far channel) away from the light source.…”

Section: Data Acquisitionmentioning

confidence: 99%

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Shirvan

Setarehdan

Nasrabadi

2018

Int Clin Neurosci J

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Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health. Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination. Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states. Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress.

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“…Functional Near-Infrared spectroscopy is a method for investigating brain activity noninvasively (Berivanlou, Setarehdan, & Noubari, 2014;Ferrari & Quaresima, 2012;Hemmati, Setarehdan, & Noubari, 2012;Rahimpour, Pollonini, Comstock, Balasubramaniam, & Bortfeld, 2020;Scholkmann et al, 2014). This low-cost imaging method has a lot of applications in the field of neuroscience, including infant cerebral hemodynamic monitoring; classification of chronic diseases; stress level, and mental workload assessment; IQ estimation (Dadgostar, Setarehdan, Shahzadi, & Akin, 2018;Firooz & Setarehdan, 2019;Hakimi & Setarehdan, 2018;Jahani, Berivanlou, Rahimpour, & Setarehdan, 2015;Mirbagheri, Hakimi, Ebrahimzadeh, & Setarehdan, 2020b, 2020aRahimpour, Dadashi, Soltanian-Zadeh, & Setarehdan, 2017;Rahimpour, Noubari, & Kazemian, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Functional near-infrared spectroscopy is a method to investigate brain activity noninvasively ( Berivanlou et al, 2014 ; Ferrari & Quaresima, 2012 ; Hemmati et al, 2012 ; Rahimpour et al, 2020 ; Scholkmann et al, 2014 ). This low-cost imaging method has many applications in the field of neuroscience, including infant cerebral hemodynamic monitoring; classification of chronic diseases; stress level, and mental workload assessment, and intelligence quotient (IQ) estimation ( Dadgostar et al, 2018 ; Firooz & Setarehdan, 2019 ; Hakimi & Setarehdan, 2018 ; Jahani et al, 2015 ; Mirbagheri et al, 2020b , 2020a ; Rahimpour et al, 2017 ; Rahimpour et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Comparison of Analytical Solution and Finite Element Method for Investigation of Near-infrared Light Propagation in Brain Tissue Model

Borjkhani¹,

Setarehdan²

2023

BCN

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Introduction: Functional Near-Infrared Spectroscopy (fNIRS) is an imaging method in which light source and detector are installed on the head; consequently, re-emission of light from human skin contains information about cerebral hemodynamic alteration. The spatial probability distribution profile of photons penetrating tissue at a source spot, scattering into the tissue, and being released at an appropriate detector position, represents the spatial sensitivity. Method: Modeling light propagation in a human head is essential for quantitative near-infrared spectroscopy and optical imaging. The specific form of the distribution of light is obtained using the theory of perturbation. Analytical solution of the perturbative Diffusion Equation (DE) and Finite Element Method (FEM) in a Slab media (similar to the human head) makes it possible to study light propagation due to absorption and scattering of brain tissue. Results: The simulation result indicates that sensitivity is slowly decreasing in the deep area, and the sensitivity below the source and detector is the highest. The depth sensitivity and computation time of both Analytical and FEM methods are compared. The simulation time of the analytical approach is four orders of magnitude faster than the FEM. Conclusion: In this paper, an analytical solution and FEM methods performance when applied to the diffusion equation for heterogeneous media with a single spherical defect are compared. The depth sensitivity, along with the computation time of simulation, has been investigated for both methods. For simple and Slab-like human brain models, the analytical solution is the right candidate. Whenever the brain model is sophisticated, it is possible to use FEM methods, but it costs higher computation time.

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Multi-channel Near-Infrared Spectroscopy (NIRS) system for noninvasive monitoring of brain activity

Cited by 10 publications

References 8 publications

Attention level quantification during a modified stroop color word experiment: an fNIRS based study

Attention level quantification during a modified stroop color word experiment: an fNIRS based study

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Quantitative Comparison of Analytical Solution and Finite Element Method for Investigation of Near-infrared Light Propagation in Brain Tissue Model

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