A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.

Overview

Authors: Vincenzo Ronca^1,2, Rossella Capotorto³, Andrea Giorgi^2,3, Francesca Dello Iacono³, Marianna Cecchetti³, Linda Napoletano⁴, Francois Brambati⁴, Gianluca Borghini^2,5, Pietro Aricò^1,2, Gianluca Di Flumeri^2,5

ORCID iDs: Vincenzo Ronca, Rossella Capotorto, Francesca Dello Iacono, Marianna Cecchetti, Francois Brambati, Gianluca Borghini, Pietro Aricò, Gianluca Di Flumeri

Institutions: Sapienza University of Rome (Italy); Deep Blue (Italy) (Italy)

Journal: PloS one, volume 21, issue 5, article e0348608

Dates: received 9 December 2025; accepted 17 April 2026; published online 26 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0348608 · PMID 42189818 · PMCID PMC13210396 · OpenAlex W7162394099

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: EEG (modality), human (organism)

Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials, Connectivity, Physiology & signal measures

MeSH: Attention*, Automobile Driving*, Electroencephalography*, Information Theory*, Adult, Dual-Task Tests, Female, Humans, Male, Psychomotor Performance, Young Adult (* major topic)

Journal subjects: Biology and Life Sciences, Neuroscience, Cognitive Science, Cognitive Psychology, Attention, Psychology, Social Sciences, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Physiology, Electrophysiology, Neurophysiology, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Perception, Sensory Perception, Vision, Ecology and Environmental Sciences, Terrestrial Environments, Urban Environments, Behavior, Cognition

Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Italian Ministry of University and Research (FIS-2023-02088, CUP: B53C24009580001)

Citations: not cited yet (Europe PMC); 96 references in the paper

Abstract

Monitoring how attention is distributed across concurrent task demands is fundamental in daily life contexts such as mobility, education and industrial control. However, existing neurophysiological measures, typically based on univariate EEG markers, are sensitive to generic cognitive load, visual complexity, or motor activity, and therefore lack specificity for attentional splitting. Here, we introduce the Attentional Split Index (ASI), a novel EEG-based metric that employ Mutual Information (MI) theory, designed to quantify the coordinated modulation of multiple neurometrics that emerges when attention is divided across tasks. Twenty-five participants completed a realistic driving protocol combining a main driving task in two different environments (Urban, Highway) with four types of attentional-split demands, i.e., Focused, Auditory Continuous Performance Test (ACPT), Matrix, Surrogate Reference Task (SURT). Traditional neurometrics (parietal alpha, inverse frontal beta, frontal theta/beta ratio) exhibited partial sensitivity to task demands but failed to selectively reflect attentional splitting. In contrast, the ASI showed a robust and systematic increase across conditions, distinguishing not only explicit multitasking segments but also subtler differences between Urban and Highway focused driving. Eye-tracking and subjective distraction rating showed a strong and significant correlation with the ASI (Urban: rET = 0.515 and rSUB = 0.744; Highway: rET = 0.357 and rSUB = 0.673; all p < 10−2), confirming high behavioural and phenomenological coherence. Surrogate analyses demonstrated that ASI effects were absent when temporal coordination across neurometrics was artificially disrupted, and that real ASI exceeded surrogate values in the vast majority of participants during multitasking but not during eyes-open baseline. Together, these findings establish the ASI as a specific, robust, and ecologically coherent neural marker of attentional splitting, with promising implications for neuroergonomics and next-generation adaptive human–machine systems.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

zenodo:19063664 — at Zenodo; found in “Data Availability”

Data Availability

The data that support the findings of this study are available from the Zenodo repository (direct link: doi.org/10.5281/zenodo.19063664).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 11 MeSH terms, 1 funder, 93 references.

Cite

This paper

Ronca, V., Capotorto, R., Giorgi, A., Dello Iacono, F., Cecchetti, M., Napoletano, L., Brambati, F., Borghini, G., Aricò, P., & Di Flumeri, G. (2026). A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel. PloS one, 21(5), e0348608. https://doi.org/10.1371/journal.pone.0348608

BibTeX

@article{ronca2026novel, author = {Ronca, Vincenzo and Capotorto, Rossella and Giorgi, Andrea and Dello Iacono, Francesca and Cecchetti, Marianna and Napoletano, Linda and Brambati, Francois and Borghini, Gianluca and Aricò, Pietro and Di Flumeri, Gianluca}, title = {{A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel}}, journal = {PloS one}, year = {2026}, month = may, volume = {21}, number = {5}, pages = {e0348608}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0348608}, url = {https://doi.org/10.1371/journal.pone.0348608}, pmid = {42189818}, pmcid = {PMC13210396} }

RIS

TY - JOUR AU - Ronca, Vincenzo AU - Capotorto, Rossella AU - Giorgi, Andrea AU - Dello Iacono, Francesca AU - Cecchetti, Marianna AU - Napoletano, Linda AU - Brambati, Francois AU - Borghini, Gianluca AU - Aricò, Pietro AU - Di Flumeri, Gianluca TI - A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/05/26 VL - 21 IS - 5 SP - e0348608 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0348608 UR - https://doi.org/10.1371/journal.pone.0348608 LA - en ER -

CSL-JSON

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