ArticleScientific reports2019
A Machine Learning Approach for the Identification of a Biomarker of Human Pain using fNIRS.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed, 117 citations in OpenAlex.
- Cedalion tutorial: a Python-based framework for comprehensive analysis of multimodal fNIRS and DOT from the lab to the everyday world.Neurophotonics · 2026Article
- Connectivity-Based Pain Recognition from fNIRS: Parsimonious Subject-Independent Classification.Sensors (Basel, Switzerland) · 2026Article
- AI-Driven Hemodynamic Detection of Self-Induced Daydreaming With EMG-Based Physiological Triggers During Pre- and Post-Prandial States Using fNIRS and EGG.CNS neuroscience & therapeutics · 2026Article
- Article
- A new biomarker for screening Hiesho (cold hypersensitivity) in young females through measurement of the foot temperature-recovery rate after a cooling test.BMC women's health · 2025Article
- Influence of Pre-Existing Pain on the Body's Response to External Pain Stimuli: Experimental Study.JMIR biomedical engineering · 2025Article
- Classifying social and physical pain from multimodal physiological signals using machine learning.Scientific reports · 2025Article
- The need for epistemic humility in AI-assisted pain assessment.Medicine, health care, and philosophy · 2025Article
- NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition.Sensors (Basel, Switzerland) · 2025Article
- Pain classification using functional near infrared spectroscopy and assessment of virtual reality effects in cancer pain management.Scientific reports · 2025Article
- Review
- Innovations in acute and chronic pain biomarkers: enhancing diagnosis and personalized therapy.Regional anesthesia and pain medicine · 2025Review
- Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.PloS one · 2025Article
- Leveraging voice biomarkers to quantify chronic pain: a rapid review.Frontiers in pain research (Lausanne, Switzerland) · 2025Review
- Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.Neurophotonics · 2024Article
- AI in cellular engineering and reprogramming.Biophysical journal · 2024Review
- Investigating the relationship between CSAT scores and prefrontal fNIRS signals during cognitive tasks using a quantum annealing algorithm.Scientific reports · 2024Article
- Use of cortical hemodynamic responses in digital therapeutics for upper limb rehabilitation in patients with stroke.Journal of neuroengineering and rehabilitation · 2024Article
- Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain is a highly unpleasant sensory and emotional experience, and no objective diagnosis test exists to assess it. In clinical practice there are two main methods for the estimation of pain, a patient's self-report and clinical judgement. However, these methods are highly subjective and the need of biomarkers to measure pain is important to improve pain management, reduce risk factors, and contribute to a more objective, valid, and reliable diagnosis. Therefore, in this study we propose the use of functional near-infrared spectroscopy (fNIRS) and machine learning for the identification of a possible biomarker of pain. We collected pain information from 18 volunteers using the thermal test of the quantitative sensory testing (QST) protocol, according to temperature level (cold and hot) and pain intensity (low and high). Feature extraction was completed in three different domains (time, frequency, and wavelet), and a total of 69 features were obtained. Feature selection was carried out according to three criteria, information gain (IG), joint mutual information (JMI), and Chi-squared (χ
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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.