scholarly journals Removing Task-Induced Superficial-Tissue Hemodynamics and Head Motion-Induced Artifacts in Functional Near-Infrared Spectroscopy

2018 ◽  
Vol 60 (4) ◽  
pp. 337-346 ◽  
Author(s):  
Hiroki Nakamura ◽  
Takanori Sato ◽  
Isao Nambu ◽  
Yasuhiro Wada
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Head Motion ◽  
Functional Near Infrared Spectroscopy ◽  
Superficial Tissue

2A2-C05 Estimation of the Head Motion Component of the functional Near-Infrared Spectroscopy (fNIRS) Data(Sense, Motion and Measurement(2))

2012 ◽  
Vol 2012 (0) ◽  
pp. _2A2-C05_1-_2A2-C05_4
Author(s):  
Kosuke TAKAHASHI ◽  
Takayuki TANAKA ◽  
Hiroyuki NARA ◽  
Shun'ichi KANEKO ◽  
Satoru KOJIMA ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Head Motion ◽  
Functional Near Infrared Spectroscopy

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

61-LB: Use of Functional Near-Infrared Spectroscopy (fNIRS) to Assess Cognitive Effort in KATP-Related Neonatal Diabetes (KATP-NDM)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 61-LB
Author(s):  
LISA R. LETOURNEAU-FREIBERG ◽  
KIMBERLY L. MEIDENBAUER ◽  
ANNA M. DENSON ◽  
PERSEPHONE TIAN ◽  
KYOUNG WHAN CHOE ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Neonatal Diabetes ◽  
Cognitive Effort ◽  
Functional Near Infrared Spectroscopy

The Use of fNIRS for Unique Contributions to Social and Affective Neuroscience

2019 ◽  
Author(s):  
Shannon Burns ◽  
Matthew D. Lieberman
Keyword(s):  
Infrared Spectroscopy ◽  
Social Interaction ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Affective Neuroscience ◽  
Functional Near Infrared Spectroscopy ◽  
Psychological Experience ◽  
The World ◽  
And Behavior ◽  
Meaningful Interaction

Social and affective neuroscience studies the neurophysiological underpinnings of psychological experience and behavior as it relates to the world around us. Yet, most neuroimaging methods require the removal of participants from their rich environment and the restriction of meaningful interaction with stimuli. In this Tools of the Trade article, we explain functional near infrared spectroscopy (fNIRS) as a neuroimaging method that can address these concerns. First, we provide an overview of how fNIRS works and how it compares to other neuroimaging methods common in social and affective neuroscience. Next, we describe fNIRS research that highlights its usefulness to the field – when rich stimuli engagement or environment embedding is needed, studies of social interaction, and examples of how it can help the field become more diverse and generalizable across participant populations. Lastly, this article describes how to use fNIRS for neuroimaging research with points of advice that are particularly relevant to social and affective neuroscience studies.

The application of functional near-infrared spectroscopy in social cognitive neuroscience

2017 ◽  
Vol 25 (5) ◽  
pp. 731
Author(s):  
Peixia YE ◽  
Ruida ZHU ◽  
Honghong TANG ◽  
Xiaoqin MAI ◽  
Chao LIU
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Cognitive Neuroscience ◽  
Social Cognitive ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Social Cognitive Neuroscience

Toward Neuroscience of the Everyday World (NEW) using functional near-infrared spectroscopy

2021 ◽  
Vol 18 ◽  
pp. 100272
Author(s):  
Alexander von Lühmann ◽  
Yilei Zheng ◽  
Antonio Ortega-Martinez ◽  
Swathi Kiran ◽  
David C. Somers ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
The Everyday ◽  
Everyday World

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

scholarly journals Comparison of Brain Activation Patterns during Olfactory Stimuli between Recovered COVID-19 Patients and Healthy Controls: A Functional Near-Infrared Spectroscopy (fNIRS) Study

2021 ◽  
Vol 11 (8) ◽  
pp. 968
Author(s):  
Roger C. Ho ◽  
Vijay K. Sharma ◽  
Benjamin Y. Q. Tan ◽  
Alison Y. Y. Ng ◽  
Yit-Shiang Lui ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Time Interval ◽  
Healthy Controls ◽  
Functional Near Infrared Spectroscopy ◽  
Activation Patterns ◽  
Identification Test ◽  
Olfactory Stimuli ◽  
Preliminary Study

Impaired sense of smell occurs in a fraction of patients with COVID-19 infection, but its effect on cerebral activity is unknown. Thus, this case report investigated the effect of COVID-19 infection on frontotemporal cortex activity during olfactory stimuli. In this preliminary study, patients who recovered from COVID-19 infection (n = 6) and healthy controls who never contracted COVID-19 (n = 6) were recruited. Relative changes in frontotemporal cortex oxy-hemoglobin during olfactory stimuli was acquired using functional near-infrared spectroscopy (fNIRS). The area under curve (AUC) of oxy-hemoglobin for the time interval 5 s before and 15 s after olfactory stimuli was derived. In addition, olfactory function was assessed using the Sniffin’ Sticks 12-identification test (SIT-12). Patients had lower SIT-12 scores than healthy controls (p = 0.026), but there were no differences in oxy-hemoglobin AUC between healthy controls and patients (p > 0.05). This suggests that past COVID-19 infection may not affect frontotemporal cortex function, and these preliminary results need to be verified in larger samples.

Sign in / Sign up

Export Citation Format

Share Document