Connectivity-Based Pain Recognition from fNIRS: Parsimonious Subject-Independent Classification.

Mohammadreza Safari, Maryam Ghahramani, Raul Fernandez Rojas

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mohammadreza SafariBioSIS (Biosensing & Intelligent Systems) Lab, Centre for Intelligent Computing and Systems, University of Canberra, Bruce, ACT 2617, Australia.ORCID 0009-0009-9306-3820

Maryam GhahramaniBioSIS (Biosensing & Intelligent Systems) Lab, Centre for Intelligent Computing and Systems, University of Canberra, Bruce, ACT 2617, Australia.ORCID 0000-0002-8324-3870

Raul Fernandez RojasBioSIS (Biosensing & Intelligent Systems) Lab, Centre for Intelligent Computing and Systems, University of Canberra, Bruce, ACT 2617, Australia.ORCID 0000-0002-8393-4241

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate pain assessment remains challenging, because pain is subjective, and common clinical tools rely on self-report or observation. Functional near-infrared spectroscopy (fNIRS) provides a non-invasive window into cortical haemodynamics and shows promise for objective pain assessment. In this study, we propose a subject-independent framework that models pain states using functional and effective connectivity features derived from fNIRS signals. Using the AI4PAIN dataset (65 participants, 24 channels), we extracted correlation, partial correlation, coherence, and Granger causality matrices from haemodynamic signals (HbO

Indexed as

PainPain MeasurementFemaleHemodynamicsHumansMaleSpectroscopy, Near-Infraredfeature selectionfNIRSfunctional and effective connectivityleave-one-subject-outmachine learningmodel parsimonypain assessment

Identifiers

PMID42197756

DOI10.3390/s26102947

PMCPMC13210414

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.