Evidence map›Paper›PMID 36527634›Full record

ArticleAdvances in experimental medicine and biology2022

Machine Learning Distinguishes Familiar from Unfamiliar Pairs of Subjects Performing an Eye Contact Task: A Systemic Physiology Augmented Functional Near-Infrared Spectroscopy Hyperscanning Study.

S Guglielmini, G Bopp, V L Marcar, Felix Scholkmann, M Wolf

Open access · greenAbstract read
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In one paragraph

Article in Advances in experimental medicine and biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 30% of its field
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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

S GuglielminiBiomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. Sabino.Guglielmini@usz.ch.
G BoppBiomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
V L MarcarBiomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Felix ScholkmannBiomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
M WolfBiomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
University Hospital of Zurich · CHUniversity of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEye contact is an important aspect of human communication and social interactions. Changes in brain and systemic physiological activity associated with interactions between humans can be measured with systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) hyperscanning, enabling inter-brain and inter-body synchronisation to be determined. In a previous study, we found that pairs of subjects that are socially connected show higher brain and body synchrony.

aimTo enable a deeper understanding, our aim was to build and automatically detect the best set of features to distinguish between two different groups (familiar and unfamiliar pairs). MATERIAL AND

methodsWe defined several features based on the Spearman correlation and wavelet transform coherence (WTC) of biosignals measured on 23 pairs of subjects (13 familiar and 10 unfamiliar pairs) during eye contact for 10 min. Additional custom features that identify the maximum brain-to-body coupling instants between pairs were generated.

resultsAfter testing on combinations of different feature extraction methods, four subsets of features with the strongest discrimination power were taken into account to train a decision tree (DT) machine learning (ML) algorithm. We have obtained 95.65% classification accuracy using a leave-one-out cross-validation. The coupling features which represent the two maximum mean values resulting from the sum of 7 time-dependent WTC signals (oxyhaemoglobin concentration of the right prefrontal region, total haemoglobin concentration of the left and right prefrontal region, heart rate, electrodermal activity on the left and right wrist, and skin temperature on the right wrist) played an essential role in the classification accuracy.

conclusionTraining the DT-ML algorithm with combined brain and systemic physiology data provided higher accuracy than training it only with brain or systemic data alone. The results demonstrate the power of the SPA-fNIRS hyperscanning approach and the potential in applying ML to investigate the strength of social bonds in a wide range of social interaction contexts.

Indexed as

OxyhemoglobinsSpectroscopy, Near-InfraredBrain MappingHumansMachine LearningPrefrontal CortexOxyhemoglobinsCross-frequency time-dependent wavelet transform coherenceEye contactSocial interactionsSPA-fNIRS hyperscanning

Identifiers

PMID36527634
OpenAlexW4312019886

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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.