Reduction of Delay in Detecting Initial Dips from Functional Near-Infrared Spectroscopy Signals Using Vector-Based Phase Analysis

In this paper, we present a systematic method to reduce the time lag in detecting initial dips using a vector-based phase diagram and an autoregressive moving average with exogenous signals (ARMAX) model-based [Formula: see text]-step-ahead prediction algorithm. With functional near-infrared spectroscopy (fNIRS), signals related to mental arithmetic and right-hand clenching are acquired from the prefrontal and left primary motor cortices, respectively. The interrelationship between oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin and cerebral oxygen exchange are related to initial dips. Specifically, a threshold value from the resting state hemodynamics is incorporated, as a decision criterion, into the vector-based phase diagram to determine the occurrence of initial dips. To further reduce the time lag, a [Formula: see text]-step-ahead prediction method is applied to predict the occurrence of the dips. A combination of the threshold criterion and the prediction method resulted in the delay time of about 0.9[Formula: see text]s. The results demonstrate that rapid detection of initial dip is possible and therefore can be used for real-time brain–computer interfacing.

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Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

Computational Intelligence and Neuroscience ◽

10.1155/2016/5480760 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 57

Author(s):

Noman Naseer ◽

Nauman Khalid Qureshi ◽

Farzan Majeed Noori ◽

Keum-Shik Hong

Keyword(s):

Infrared Spectroscopy ◽

Discriminant Analysis ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Continuous Wave ◽

Mental Arithmetic ◽

Statistical Significance ◽

Support Vector ◽

Functional Near Infrared Spectroscopy ◽

Linear Discriminant

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA),k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that thepvalues were statistically significant relative to all of the other classifiers (p< 0.005) using HbO signals.

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Complexity Analysis on Functional-Near Infrared Spectroscopy Time Series: a Preliminary Study on Mental Arithmetic

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ◽

10.1109/embc44109.2020.9176079 ◽

2020 ◽

Author(s):

Ameer Ghouse ◽

Mimma Nardelli ◽

Vincenzo Catrambone ◽

Gaetano Valenza

Keyword(s):

Time Series ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Complexity Analysis ◽

Mental Arithmetic ◽

Functional Near Infrared Spectroscopy ◽

Preliminary Study

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A DAQ-Device-Based Continuous Wave Near-Infrared Spectroscopy System for Measuring Human Functional Brain Activity

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/107320 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Gang Xu ◽

Xiaoli Li ◽

Duan Li ◽

Xiaomin Liu

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Continuous Wave ◽

Mental Arithmetic ◽

Valsalva Maneuver ◽

Light Sources ◽

Functional Near Infrared Spectroscopy ◽

Quadrature Demodulation ◽

Custom Made

In the last two decades, functional near-infrared spectroscopy (fNIRS) is getting more and more popular as a neuroimaging technique. The fNIRS instrument can be used to measure local hemodynamic response, which indirectly reflects the functional neural activities in human brain. In this study, an easily implemented way to establish DAQ-device-based fNIRS system was proposed. Basic instrumentation components (light sources driving, signal conditioning, sensors, and optical fiber) of the fNIRS system were described. The digital in-phase and quadrature demodulation method was applied in LabVIEW software to distinguish light sources from different emitters. The effectiveness of the custom-made system was verified by simultaneous measurement with a commercial instrument ETG-4000 during Valsalva maneuver experiment. The light intensity data acquired from two systems were highly correlated for lower wavelength (Pearson’s correlation coefficientr= 0.92,P< 0.01) and higher wavelength (r= 0.84,P< 0.01). Further, another mental arithmetic experiment was implemented to detect neural activation in the prefrontal cortex. For 9 participants, significant cerebral activation was detected in 6 subjects (P< 0.05) for oxyhemoglobin and in 8 subjects (P< 0.01) for deoxyhemoglobin.

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Neuronal Activation Detection Using Vector Phase Analysis with Dual Threshold Circles: A Functional Near-Infrared Spectroscopy Study

International Journal of Neural Systems ◽

10.1142/s0129065718500314 ◽

2018 ◽

Vol 28 (10) ◽

pp. 1850031 ◽

Cited By ~ 37

Author(s):

Amad Zafar ◽

Keum-Shik Hong

Keyword(s):

Phase Diagram ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Polar Coordinates ◽

Functional Near Infrared Spectroscopy ◽

Linear Discriminant ◽

Orthogonal Components ◽

Initial Dip ◽

Hr Function

In this paper, a new vector phase diagram differentiating the initial decreasing phase (i.e. initial dip) and the delayed hemodynamic response (HR) phase of oxy-hemoglobin changes ([Formula: see text]HbO) of functional near-infrared spectroscopy (fNIRS) is developed. The vector phase diagram displays the trajectories of [Formula: see text]HbO and deoxy-hemoglobin changes ([Formula: see text]HbR), as orthogonal components, in the [Formula: see text]HbO–[Formula: see text]HbR polar coordinates. To determine the occurrence of an initial dip, dual threshold circles (an inner circle from the resting state, an outer circle from the peak values of the initial dip and the main HR) are incorporated into the phase diagram for making decisions. The proposed scheme is then applied to a brain–computer interface scheme, and its performance is evaluated in classifying two finger tapping tasks (right-hand thumb and little finger) from the left motor cortex. Three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. In classifying two tapping tasks, the signal mean and signal minimum values during 0–2.5[Formula: see text]s, as features of initial dip, are used. The linear discriminant analysis was utilized as a classifier. The experimental results show that the active brain locations of the two tasks were quite distinctive ([Formula: see text]), and moreover, spatially specific if using the initial dip map at 4[Formula: see text]s in comparison to the map of HRs at 14[Formula: see text]s. Also, the average classification accuracy was improved from 59% to 74.9% when using the phase diagram of dual threshold circles.

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

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207622 ◽

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.

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

Diabetes ◽

10.2337/db20-61-lb ◽

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

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

10.31234/osf.io/kygbm ◽

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.

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