Extraction of cognitive activity-related waveforms from functional near-infrared spectroscopy signals

Akgül, Ceyhun Burak; Sankur, B.; Akin, Ata

doi:10.1007/s11517-006-0116-3

Cited by 49 publications

(49 citation statements)

References 38 publications

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“…In this setting of the problem, the coefficients of the filter should give us the HRF waveform. Note that this approach does not put any constraints over the HRF, and effectively it averages the event-related responses for each subject [22]. Fig.…”

Section: ) Oxy-hb Resultsmentioning

confidence: 99%

“…Experiments were performed using a continuous-wave near-infrared spectroscopy device (NIROXCOPE 301) built in Biophotonics Laboratory, Bogaziçi University [21], [22]. The device is capable of transmitting near-infrared light at two wavelengths (730 and 850 nm), which are known to be able to penetrate through the scalp and probe the cerebral cortex.…”

Section: B Fnirs Data Acquisitionmentioning

confidence: 99%

“…We have chosen rectangular probe geometry for obtaining nonoverlapping areas and limited our source-to-detector distances to 2.5 cm for a better fit to the forehead. This amount of separation has been shown to reliably probe the cortical activity [22]- [26]. LEDs and detectors were placed in a flexible printed circuit board that was specially designed to fit the curvature of the forehead.…”

Section: B Fnirs Data Acquisitionmentioning

confidence: 99%

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Multilevel Statistical Inference From Functional Near-Infrared Spectroscopy Data During Stroop Interference

Çiftçi

Sankur

Kahya

et al. 2008

IEEE Trans. Biomed. Eng.

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Abstract-Functional near-infrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy-and deoxy-hemoglobin (oxy-Hb and deoxy-Hb) in the brain. An important consideration in fNIRS-based neuroimaging modality is to conduct group-level analysis from a set of time series measured from a group of subjects. We investigate the feasibility of multilevel statistical inference for fNIRS. As a case study, we search for hemodynamic activations in the prefrontal cortex during Stroop interference. Hierarchical general linear model (GLM) is used for making this multilevel analysis. Activation patterns both at the subject and group level are investigated on a comparative basis using various classical and Bayesian inference methods. All methods showed consistent left lateral prefrontal cortex activation for oxy-Hb during interference condition, while the effects were much less pronounced for deoxy-Hb. Our analysis showed that mixed effects or Bayesian models are more convenient for faithful analysis of fNIRS data. We arrived at two important conclusions. First, fNIRS has the capability to identify activations at the group level, and second, the mixed effects or Bayesian model is the appropriate mechanism to pass from subject to group-level inference.Index Terms-General linear model (GLM), near-infrared spectroscopy, statistical inference, Stroop task.

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Section: ) Oxy-hb Resultsmentioning

confidence: 99%

Section: B Fnirs Data Acquisitionmentioning

confidence: 99%

Section: B Fnirs Data Acquisitionmentioning

confidence: 99%

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Multilevel Statistical Inference From Functional Near-Infrared Spectroscopy Data During Stroop Interference

Çiftçi

Sankur

Kahya

et al. 2008

IEEE Trans. Biomed. Eng.

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“…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. Zhang et al, 2010;H.…”

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

1,471

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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“…However, most of the higher-order brain functions take place in the cortex, and therefore, it is an important target for research studies. Functional near infrared spectroscopy has been used in a number of brain studies, from motor area activation, [1][2][3][4] to visual 3,5,6 and cognitive [7][8][9][10][11][12] stimulations.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Mental Workload Levels in Human Subjects With Functional Near-Infrared Spectroscopy

Sassaroli

Zheng

Hirshfield

et al. 2008

J. Innov. Opt. Health Sci.

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We have applied functional near-infrared spectroscopy (fNIRS) to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex. We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks. To quantify the potential of fNIRS for mental workload discrimination, we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin (HbO 2 ) and deoxyhemoglobin (HbR) concentration changes associated with the working memory tasks. We have found classification success rates in the range of 44%-72%, which are significantly higher than the corresponding chance level (for random data) of 19.1%. This work shows the potential of fNIRS for mental workload classification, especially when more parameters (rather than just the amplitude of concentration changes used here) and more sophisticated classification algorithms (rather than the simple 3-nearest neighbor algorithm used here) are considered and optimized for this application.

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Extraction of cognitive activity-related waveforms from functional near-infrared spectroscopy signals

Cited by 49 publications

References 38 publications

Multilevel Statistical Inference From Functional Near-Infrared Spectroscopy Data During Stroop Interference

Multilevel Statistical Inference From Functional Near-Infrared Spectroscopy Data During Stroop Interference

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

Discrimination of Mental Workload Levels in Human Subjects With Functional Near-Infrared Spectroscopy

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