ArticleNeurophotonics2016
Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy.
Article in Neurophotonics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Users evaluating physical, virtual, and mixed reality prototypes exhibit differential DLPFC brain activity.Scientific reports · 2025Article
- fNIRS, EEG, ECG, and GSR reveal an effect of complex, dynamically changing environments on cognitive load, affective state, and performance, but not physiological stress.Frontiers in human neuroscience · 2025Article
- A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.Neurophotonics · 2024Article
- Artifact Management for Cerebral Near-Infrared Spectroscopy Signals: A Systematic Scoping Review.Bioengineering (Basel, Switzerland) · 2024Article
- Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels.Neurophotonics · 2023Article
- How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS?Neurophotonics · 2023Article
- Functional connectivity response to acute pain assessed by fNIRS is associated with BDNF genotype in fibromyalgia: an exploratory study.Scientific reports · 2022Article
- Deep learning-based motion artifact removal in functional near-infrared spectroscopy.Neurophotonics · 2022Article
- Statistical approaches to temporal and spatial autocorrelation in resting-state functional connectivity in mice measured with optical intrinsic signal imaging.Neurophotonics · 2022Article
- Review
- Optical imaging and spectroscopy for the study of the human brain: status report.Neurophotonics · 2022Article
- Vocal brain development in infants of mothers with serious mental illness (CAPRI-Voc): study protocol.BMJ open · 2022Article
- Observational
- Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review.Frontiers in neuroscience · 2022Review
- Comparison of functional activation responses from the auditory cortex derived using multi-distance frequency domain and continuous wave near-infrared spectroscopy.Neurophotonics · 2021Article
- Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research.Brain sciences · 2021Review
- Variability in atlas registration of optical intrinsic signal imaging and its effect on functional connectivity analysis.Journal of the Optical Society of America. A, Optics, image science, and vision · 2021Article
- Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI).Frontiers in neurorobotics · 2021Article
- Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies.Neurophotonics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.