Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy

Hu, Xiao‐Su; Hong, Keum–Shik; Ge, Shuzhi Sam; Jeong, Myung-Yung

doi:10.1186/1475-925x-9-82

Cited by 128 publications

(98 citation statements)

References 46 publications

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“…The use of a model-based fNIRI analysis approach by means of the general linear model (GLM) methodology (Bullmore et al, 1996;Friston et al, 1995;Worsley and Friston, 1995) was reported by several authors (e.g. Boas et al, 2003;Çiftçi et al, 2008;Fekete et al, 2011;Hu et al, 2010;Imai et al, 2012;Koh et al, 2007;Moriai-Izawa et al, 2012;Plichta et al, 2007a,b;Schroeter et al, 2004;Tsuzuki et al, 2012;Ye et al, 2009). GLM is a statistical linear model explaining data as a linear combination of an explanatory variable plus an error term.…”

Section: Multivariate Methods Of Typementioning

confidence: 99%

“…GLM is a statistical linear model explaining data as a linear combination of an explanatory variable plus an error term. How to combine GLM with a Kalman estimator and to analyze fNIRI data was shown by Hu et al (2010). Methods using independent component analysis (ICA) (Akgül et al, 2006;Katura et al, 2008;Kohno et al, 2007;Markham et al, 2009;Medvedev et al, 2008;Morren et al, 2004;Schelkanova and Toronov, 2012) or principal component analysis (PCA) H.…”

Section: Multivariate Methods Of Typementioning

confidence: 99%

See 1 more Smart Citation

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Scholkmann¹,

Kleiser²,

Metz³

et al. 2014

NeuroImage

1,523

1,394

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This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. This year marks the 20th anniversary of functional near-infrared spectroscopy and imaging (fNIRS/fNIRI). As the vast majority of commercial instruments developed until now are based on continuous wave technology, the aim of this publication is to review the current state of instrumentation and methodology of continuous wave fNIRI. For this purpose we provide an overview of the commercially available instruments and address instrumental aspects such as light sources, detectors and sensor arrangements. Methodological aspects, algorithms to calculate the concentrations of oxy-and deoxyhemoglobin and approaches for data analysis are also reviewed. From the single-location measurements of the early years, instrumentation has progressed to imaging initially in two dimensions (topography) and then three (tomography). The methods of analysis have also changed tremendously, from the simple modified Beer-Lambert law to sophisticated image reconstruction and data analysis methods used today. Due to these advances, fNIRI has become a modality that is widely used in neuroscience research and several manufacturers provide commercial instrumentation. It seems likely that fNIRI will become a clinical tool in the foreseeable future, which will enable diagnosis in single subjects. Contents

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Section: Multivariate Methods Of Typementioning

confidence: 99%

Section: Multivariate Methods Of Typementioning

confidence: 99%

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Scholkmann¹,

Kleiser²,

Metz³

et al. 2014

NeuroImage

1,523

1,394

View full text Add to dashboard Cite

show abstract

“…1), which receives r ftg as input and uses r ft−1g to r ft−Pg as predictors. Equations (4)- (13) are then used to update the AR coefficients in the second Kalman model. Similar to the first Kalman model, the state update matrix (A) is identity (e.g., a random walk model).…”

Section: A Kalman Estimatormentioning

confidence: 99%

“…Due to the portability and high sample rate of fNIRS measurements, several researchers have previously explored the use of fNIRS in real-time assessments of brain activity, [10][11][12][13][14][15][16][17] biofeedback, 18 and brain-computer interfacing applications. 15,16,[19][20][21][22][23][24] These real-time applications, however, must contend with noise and artifacts often contained within the NIRS data.…”

Section: Introductionmentioning

confidence: 99%

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

et al. 2016

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Abstract. Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology. These two factors can reduce the sensitivity and specificity of detecting hemodynamic changes. In a previous work, we showed that these factors could be mitigated by autoregressive-based prewhitening followed by the application of an iterative reweighted least squares algorithm offline. This current work extends these same ideas to real-time analysis of brain signals by modifying the linear Kalman filter, resulting in an algorithm for online estimation that is robust to systemic physiology and motion artifacts. We evaluated the performance of the proposed method via simulations of evoked hemodynamics that were added to experimental resting-state data, which provided realistic fNIRS noise. Last, we applied the method post hoc to data from a standing balance task. Overall, the new method showed good agreement with the analogous offline algorithm, in which both methods outperformed ordinary least squares methods.

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“…But whereas some physiological noises (i.e., respiratory and cardiac activities) can be removed using a low-pass filter (LPF), 20 low-frequency (e.g., 0.15 Hz) oscillatory noises and motion artifacts cannot. Alternatively, unwanted noises and artifacts from fNIRS signals can be removed using linear regression, 21 least mean squares adaptive filtering, 22 and ICA. [23][24][25][26] Continuous-wave-type fNIRS measures the optical intensity changes 27 at two wavelengths, 28 which results are then converted to hemodynamic concentration changes using the modified Beer-Lambert law.…”

Section: Introductionmentioning

confidence: 99%

Noise reduction in functional near-infrared spectroscopy signals by independent component analysis

Santosa

Hong

Kim

et al. 2013

Review of Scientific Instruments

Self Cite

146

103

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Functional near-infrared spectroscopy (fNIRS) is used to detect concentration changes of oxy-hemoglobin and deoxy-hemoglobin in the human brain. The main difficulty entailed in the analysis of fNIRS signals is the fact that the hemodynamic response to a specific neuronal activation is contaminated by physiological and instrument noises, motion artifacts, and other interferences. This paper proposes independent component analysis (ICA) as a means of identifying the original hemodynamic response in the presence of noises. The original hemodynamic response was reconstructed using the primary independent component (IC) and other, less-weighting-coefficient ICs. In order to generate experimental brain stimuli, arithmetic tasks were administered to eight volunteer subjects. The t-value of the reconstructed hemodynamic response was improved by using the ICs found in the measured data. The best t-value out of 16 low-pass-filtered signals was 37, and that of the reconstructed one was 51. Also, the average t-value of the eight subjects’ reconstructed signals was 40, whereas that of all of their low-pass-filtered signals was only 20. Overall, the results showed the applicability of the ICA-based method to noise-contamination reduction in brain mapping.

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Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy

Cited by 128 publications

References 46 publications

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

Noise reduction in functional near-infrared spectroscopy signals by independent component analysis

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