Evidence map›Paper›PMID 41696456›Full record

SynthesisFrontiers in neuroergonomics2025

Combining EEG and eye-tracking for cognitive and physiological states monitoring: a systematic review.

Maria Rivas-Vidal, Alberto Calvo Cordoba, Cecilia E García Cena, Fernando Daniel Farfán

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in neuroergonomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
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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

4 authors.

Maria Rivas-VidalEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, Madrid, Spain.
Alberto Calvo CordobaIndra Sistemas, S.A., Madrid, Spain.
Cecilia E García CenaCentro de Automática y Robótica UPM-CSIC, Escuela Técnica Superior de Ingeniería y Diseño Industrial, Universidad Politécnica de Madrid, Madrid, Spain.
Fernando Daniel FarfánInstituto de Bioingeniería, Universidad Miguel Hernández, Alicante, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring situational awareness is critical in highly demanding environments where sustained attention and vigilance are essential for safety and performance. Electroencephalography (EEG) and eye-tracking (ET) provide complementary insights into the perceptual layer of situational awareness, capturing neural and ocular signatures of information processing, attention, and fatigue. However, studies have typically examined perception-related conditions such as workload, fatigue, stress, and drowsiness in isolation, limiting understanding of their shared and distinct physiological patterns. This systematic review synthesizes findings from studies that recorded EEG and ET concurrently to investigate perception-related conditions. Following the PRISMA 2020 statement, five databases were searched, and 47 studies met the inclusion criteria. The most frequently reported EEG features included theta, alpha, and beta activity, while ET metrics commonly involved fixation patterns, pupil diameter, blink dynamics, and percentage of eyes closed (PERCLOS). Across studies, fatigue, mental workload, and stress exhibited overlapping physiological signatures, although multimodal data helped differentiate these closely related states. Drowsiness and vigilance decrement appeared along a shared continuum, with microsleeps showing distinct physiological profiles. Classification models generally achieved higher accuracy when integrating EEG and ET features than when using either modality alone. This review highlights the potential of concurrent EEG and ET monitoring for improving the detection of perception-related conditions and for disambiguating closely related states. These findings also support the need for standardized multimodal protocols and real-time multimodal classification models to strengthen cognitive-state monitoring, operational performance, and error prevention in high-risk domains.

Indexed as

drowsinessfatiguemental workloadmind wanderingperceptionsituational awarenessstressvigilance

Identifiers

PMID41696456
PMCPMC12895110

What OpenQuestion holds

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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.