Motion Artifact Cancellation in NIR Spectroscopy Using Wiener Filtering

İzzetoğlu, Meltem; Devaraj, Ajit; Bunce, Scott C.; Onaral, Banu

doi:10.1109/tbme.2005.845243

Cited by 154 publications

(146 citation statements)

References 7 publications

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“…Furthermore, unintentionally performed stimulus/task-evoked movements of the head may lead to movement artifacts. These are mostly due to changes in light coupling to the tissue, but part could also be due to genuine changes in CBF/CBV, in the fNIRI signal (Izzetoglu et al, 2005;Kurihara et al, 2003;Özgören et al, 2012;Scholkmann et al, 2010;Takahashi et al, 2012;Tax et al, 2011). That also the saliva secretion rate is accompanied with changes in extracerebral hemodynamics was initially shown by Sato et al (2011) and recently confirmed by Matsumoto et al (2012).…”

Section: Classification Of Signal Componentsmentioning

confidence: 99%

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

Scholkmann¹,

Kleiser²,

Metz³

et al. 2014

NeuroImage

1,481

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: Classification Of Signal Componentsmentioning

confidence: 99%

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

Scholkmann¹,

Kleiser²,

Metz³

et al. 2014

NeuroImage

1,481

1,394

View full text Add to dashboard Cite

show abstract

“…There are many advanced noise reduction algorithms available that utilize different characteristics of the signal [17][18][19][20] . However if a subject or session can be excluded if data is irrecoverable (i.e.…”

Section: Processing the Fnir Data And Analysismentioning

confidence: 99%

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Ayaz

Shewokis

Curtin

et al. 2011

JoVE

173

167

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MazeSuite is a complete toolset to prepare, present and analyze navigational and spatial experiments 1 . MazeSuite can be used to design and edit adapted virtual 3D environments, track a participants' behavioral performance within the virtual environment and synchronize with external devices for physiological and neuroimaging measures, including electroencephalogram and eye tracking.Functional near-infrared spectroscopy (fNIR) is an optical brain imaging technique that enables continuous, noninvasive, and portable monitoring of changes in cerebral blood oxygenation related to human brain functions [2][3][4][5][6][7] . Over the last decade fNIR is used to effectively monitor cognitive tasks such as attention, working memory and problem solving [7][8][9][10][11] . fNIR can be implemented in the form of a wearable and minimally intrusive device; it has the capacity to monitor brain activity in ecologically valid environments.Cognitive functions assessed through task performance involve patterns of brain activation of the prefrontal cortex (PFC) that vary from the initial novel task performance, after practice and during retention 12 . Using positron emission tomography (PET), Van Horn and colleagues found that regional cerebral blood flow was activated in the right frontal lobe during the encoding (i.e., initial naïve performance) of spatial navigation of virtual mazes while there was little to no activation of the frontal regions after practice and during retention tests. Furthermore, the effects of contextual interference, a learning phenomenon related to organization of practice, are evident when individuals acquire multiple tasks under different practice schedules 13,14 . High contextual interference (random practice schedule) is created when the tasks to be learned are presented in a non-sequential, unpredictable order. Low contextual interference (blocked practice schedule) is created when the tasks to be learned are presented in a predictable order.Our goal here is twofold: first to illustrate the experimental protocol design process and the use of MazeSuite, and second, to demonstrate the setup and deployment of the fNIR brain activity monitoring system using Cognitive Optical Brain Imaging (COBI) Studio software 15 . To illustrate our goals, a subsample from a study is reported to show the use of both MazeSuite and COBI Studio in a single experiment. The study involves the assessment of cognitive activity of the PFC during the acquisition and learning of computer maze tasks for blocked and random orders. Two right-handed adults (one male, one female) performed 315 acquisition, 30 retention and 20 transfer trials across four days. Design, implementation, data acquisition and analysis phases of the study were explained with the intention to provide a guideline for future studies. Video LinkThe video component of this article can be found at https://www.jove.com/video/3443/ ProtocolMaze-Suite consists of three main applications; an editing program to construct maze environments (MazeMaker), a visualiz...

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“…In this study we have used accelerometer data as reference signal and the light intensity time-series as the primary input signal. Details of the technique can be found in [4]. To illustrate the utility of the methodology, a normalized least mean squares (NLMS) adaptive filter has been employed in this study to clean the motion artifact contaminated epochs (recorded by Channel 2).…”

Section: Resultsmentioning

confidence: 99%

“…Adaptive filters have been utilized to remove the unwanted noise [4] [8] and have been shown to produce a moderate improvement in signal to noise ratio (SNR). Izzetoglu et al also proposed Wiener [4] and discrete Kalman filtering [3] techniques for the removal of motion artifacts. Wiener filtering has been observed to produce a greater improvement in SNR than that realized using adaptive filtering methods while also reducing the need for additional sensors to capture some measure of the noise source.…”

Section: Introductionmentioning

confidence: 99%

A methodology for validating artifact removal techniques for fNIRS

Sweeney

Ayaz

Ward

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-fNIRS recordings are increasingly utilized to monitor brain activity in both clinical and connected health settings. These optical recordings provide a convenient measurement of cerebral hemodynamic changes which can be linked to motor and cognitive performance. Such measurements are of clinical utility in a broad range of conditions ranging from dementia to movement rehabilitation therapy. For such applications fNIRS is increasingly deployed outside the clinic for patient monitoring in the home. However, such a measurement environment is poorly controlled and motion, in particular, is a major source of artifacts in the signal, leading to poor signal quality for subsequent clinical interpretation. Artifact removal techniques are increasingly being employed with an aim of reducing the effect of the noise in the desired signal. Currently no methodology is available to accurately determine the efficacy of a given artifact removal technique due to the lack of a true reference for the uncontaminated signal. In this paper we propose a novel methodology for fNIRS data collection allowing for effective validation of artifact removal techniques. This methodology describes the use of two fNIRS channels in close proximity allowing them to sample the same measurement location; allowing for the introducing of motion artifact to only one channel while having the other free of contamination. Through use of this methodology, for each motion artifact epoch, a true reference for the uncontaminated signal becomes available for use in the development and performance evaluation of signal processing strategies. The advantage of the described methodology is demonstrated using a simple artifact removal technique with an accelerometer based reference.

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Motion Artifact Cancellation in NIR Spectroscopy Using Wiener Filtering

Cited by 154 publications

References 7 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

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

A methodology for validating artifact removal techniques for fNIRS

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