A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments
Sign Up to like & getrecommendations! Published in 2017 at "Neuroimage"
DOI: 10.1016/j.neuroimage.2017.05.001
Abstract: ABSTRACT Recent technological advances have allowed the development of portable functional Near‐Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real‐world. However, as real‐world experiments are designed to mimic everyday life… read more here.
Keywords: real world; method; functional events; data recorded ... See more keywords
Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy.
Sign Up to like & getrecommendations! Published in 2020 at "Journal of neural engineering"
DOI: 10.1088/1741-2552/abb4a4
Abstract: OBJECTIVE Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity;… read more here.
Keywords: phase transfer; connectivity; transfer entropy; symbolic phase ... See more keywords
Domain adaptation for robust workload level alignment between sessions and subjects using fNIRS
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Biomedical Optics"
DOI: 10.1117/1.jbo.26.2.022908
Abstract: Abstract. Significance: We demonstrated the potential of using domain adaptation on functional near-infrared spectroscopy (fNIRS) data to classify different levels of n-back tasks that involve working memory. Aim: Domain shift in fNIRS data is a… read more here.
Keywords: alignment; session; fnirs data; domain adaptation ... See more keywords
Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data
Sign Up to like & getrecommendations! Published in 2022 at "Neurophotonics"
DOI: 10.1117/1.nph.9.2.025003
Abstract: Abstract. Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been… read more here.
Keywords: time; regression; signals real; kalman filter ... See more keywords
A deep learning approach to remove motion artifacts in fNIRS data analysis
Sign Up to like & getrecommendations! Published in 2020 at "Brain"
DOI: 10.1364/brain.2020.bm2c.7
Abstract: We established a neural network model to efficiently remove motion artifacts during fNIRS data processing. read more here.
Keywords: deep learning; artifacts fnirs; motion artifacts; remove motion ... See more keywords
Oscillator decomposition of infant fNIRS data
Sign Up to like & getrecommendations! Published in 2022 at "PLoS Computational Biology"
DOI: 10.1371/journal.pcbi.1009985
Abstract: The functional near-infrared spectroscopy (fNIRS) can detect hemodynamic responses in the brain and the data consist of bivariate time series of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) on each channel. In this study, we… read more here.
Keywords: oscillator; time series; osc decomp; infant fnirs ... See more keywords
Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
Sign Up to like & getrecommendations! Published in 2018 at "Algorithms"
DOI: 10.3390/a11050067
Abstract: With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference… read more here.
Keywords: automated processing; data visual; visual guide; processing fnirs ... See more keywords