Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding

Kamran, Muhammad Ahmad; Hong, Keum–Shik

doi:10.1016/j.neulet.2014.07.058

Cited by 48 publications

(39 citation statements)

References 25 publications

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“…But in the case of fNIRS, it constitutes an additional challenge of the physiological noise in the optical signal. Recently, several studies have been reported to analyze fNIRS time series using existing/new and/or modified versions of existing HRF models (Abdelnour and Huppert, 2009; Hu et al, 2010; Kamran and Hong, 2013, 2014; Santosa et al, 2013; Scarpa et al, 2013; Hong and Nugyen, 2014). The approaches vary in their implementation from simple estimation algorithms to more complex adaptive algorithms (Kamran and Hong, 2013) and blind signal processing (Santosa et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…The data set has been generated with the methods described in existing literature (Prince et al, 2003; Abdelnour and Huppert, 2009)

H R F (k) = h {(k)}^{*} u (k),

\begin{matrix} left \\ h (k) = [\frac{k^{α_{1} - 1} β_{1}^{α_{1}} e^{- β_{1} k}}{Γ (α_{1})} - \frac{k^{α_{2} - 1} β_{2}^{α_{2}} e^{- β_{2} k}}{6 Γ (α_{2})}] \end{matrix}

where u is the experimental paradigm, h is the cHRF, α 1 is the delay of the response, α 2 is the delay of the undershoot, β 1 is the dispersion of the response, β 2 is the dispersion of the undershoot and Γ represents the Gamma distribution. The physiological signals in simulated data have been generated through the linear combination of three sinusoids (Abdelnour and Huppert, 2009; Kamran and Hong, 2014). The specific values of free parameters used for all 15 data sets have been listed in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies showed that localization of the cortical activation could be statistically estimated by fitting the estimated HRF to a pre-defined HRF (Hu et al, 2010; Kamran and Hong, 2014; Santosa et al, 2014). Let the optimal brain activation model is

\begin{matrix} left \\ y_{H b O}^{i} (k) = a_{0}^{*} + a_{1}^{*} H R F^{*} (k) + a_{c}^{*} \sin (2 π f_{c}^{*} k) + a_{r}^{*} \sin (2 π f_{r}^{*} k) \\ + a_{m}^{*} \sin (2 π f_{m}^{*} k) + ε^{i} (k), \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…Thus, brain functional information can be revealed by the estimation of HbO and HbR. fNIRS, with the ability to estimate both chromospheres, is a potential brain imaging modality (Kamran and Hong, 2014). Functional-magnetic resonance imaging (fMRI) (Cohen et al, 2014; Zhou et al, 2014) and electro-encephalography (EEG) are most frequently used cortical imaging modalities in past.…”

Section: Introductionmentioning

confidence: 99%

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Optimal hemodynamic response model for functional near-infrared spectroscopy

Kamran

Jeong

Mannan

2015

Front. Behav. Neurosci.

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Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650–950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > tcritical and p-value < 0.05).

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Section: Discussionmentioning

confidence: 99%

“…The data set has been generated with the methods described in existing literature (Prince et al, 2003; Abdelnour and Huppert, 2009)

H R F (k) = h {(k)}^{*} u (k),

\begin{matrix} left \\ h (k) = [\frac{k^{α_{1} - 1} β_{1}^{α_{1}} e^{- β_{1} k}}{Γ (α_{1})} - \frac{k^{α_{2} - 1} β_{2}^{α_{2}} e^{- β_{2} k}}{6 Γ (α_{2})}] \end{matrix}

Section: Methodsmentioning

confidence: 99%

\begin{matrix} left \\ y_{H b O}^{i} (k) = a_{0}^{*} + a_{1}^{*} H R F^{*} (k) + a_{c}^{*} \sin (2 π f_{c}^{*} k) + a_{r}^{*} \sin (2 π f_{r}^{*} k) \\ + a_{m}^{*} \sin (2 π f_{m}^{*} k) + ε^{i} (k), \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal hemodynamic response model for functional near-infrared spectroscopy

Kamran

Jeong

Mannan

2015

Front. Behav. Neurosci.

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“…Examples are the spatially resolved spectroscopy approach [42,43], the self-calibrating algorithm [44], or a variety of other approaches [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62]. Type 1 methods were in general developed to remove the influence of the superficial layer (skin and skull) from the measured signals but help also to reduce MAs, as demonstrated recently by Scholkmann et al [11].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

et al. 2015

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Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (i.e., hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, i.e., data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique ("acceleration-based movement artifact reduction algorithm", AMARA) is based on combining two methods: the "movement artifact reduction algorithm" (MARA, et al. Phys. Meas. 2010, 31, 649-662), and the "accelerometer-based motion artifact removal" (ABAMAR, Virtanen et al. J. Biomed. Opt. 2011, 16, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data.OPEN ACCESS Algorithms 2015, 8 1053 Scholkmann

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Multi-class fNIRS Classification of Motor Execution Tasks with Application to Brain-Computer Interfaces

Shamsi

Najafizadeh

2021

Biomedical Signal Processing

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Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding

Cited by 48 publications

References 25 publications

Optimal hemodynamic response model for functional near-infrared spectroscopy

Optimal hemodynamic response model for functional near-infrared spectroscopy

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

Multi-class fNIRS Classification of Motor Execution Tasks with Application to Brain-Computer Interfaces

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