ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2018
An fNIRS-Based Feature Learning and Classification Framework to Distinguish Hemodynamic Patterns in Children Who Stutter.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Large-scale genome-wide analyses of stuttering.Nature genetics · 2025Pooled it
- Functional Near-Infrared Spectroscopy in the Study of Speech and Language Impairment Across the Life Span: A Systematic Review.American journal of speech-language pathology · 2020Pooled it
- Single-subject detection of speech network activation using functional near-infrared spectroscopy.Neurophotonics · 2026Article
- fNIRS-STCT: A Novel Hybrid Spatial and Temporal CNN and Transformer Network for fNIRS Signal Classification.Brain topography · 2026Article
- Enhanced fNIRS-Based MCI Detection via Resting-State and Task-State Integration With Spatial-Temporal Feature Reduction.IEEE journal of translational engineering in health and medicine · 2026Article
- Stuttering: Our Current Knowledge, Research Opportunities, and Ways to Address Critical Gaps.Neurobiology of language (Cambridge, Mass.) · 2025Review
- Functional near-infrared spectroscopy in pediatric clinical research: Different pathophysiologies and promising clinical applications.Neurophotonics · 2023Article
- Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research.Frontiers in neurorobotics · 2022Review
- Consistency of children's hemodynamic responses during spontaneous speech.Neurophotonics · 2022Article
- Looking for "fNIRS Signature" in Autism Spectrum: A Systematic Review Starting From Preschoolers.Frontiers in neuroscience · 2022Review
- Exploring Relationships Among Risk Factors for Persistence in Early Childhood Stuttering.Journal of speech, language, and hearing research : JSLHR · 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
- Using a Data-Driven Approach to Estimate Second-Language Proficiency From Brain Activation: A Functional Near-Infrared Spectroscopy Study.Frontiers in neuroscience · 2020Article
- Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.Frontiers in neuroscience · 2020Article
- What Are Predictors for Persistence in Childhood Stuttering?Seminars in speech and language · 2018Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Stuttering is a communication disorder that affects approximately 1% of the population. Although 5-8% of preschool children begin to stutter, the majority will recover with or without intervention. There is a significant gap, however, in our understanding of why many children recover from stuttering while others persist and stutter throughout their lives. Detecting neurophysiological biomarkers of stuttering persistence is a critical objective of this paper. In this paper, we developed a novel supervised sparse feature learning approach to discover discriminative biomarkers from functional near infrared spectroscopy (fNIRS) brain imaging data recorded during a speech production experiment from 46 children in three groups: children who stutter ( ); children who do not stutter ( ); and children who recovered from stuttering ( ). We made an extensive feature analysis of the cerebral hemodynamics from fNIRS signals and selected a small number of important discriminative features using the proposed sparse feature learning framework. The selected features are capable of differentiating neural activation patterns between children who do and do not stutter with an accuracy of 87.5% based on a five-fold cross-validation procedure. The discovered set cerebral hemodynamics features are presented as a set of promising biomarkers to elucidate the underlying neurophysiology in children who have recovered or persisted in stuttering and to facilitate future data-driven diagnostics in these children.
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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.