Evidence map›Paper›PMID 41157499›Full record

ArticleSensors (Basel, Switzerland)2025

Wearable EEG Sensor Analysis for Cognitive Profiling in Educational Contexts.

Eleni Lekati, Georgios N Dimitrakopoulos, Konstantinos Lazaros, Panagiota Giannopoulou, Aristidis G Vrahatis, Marios G Krokidis, Panagiotis Vlamos, Spyridon Doukakis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Eleni LekatiBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0009-0006-6710-1422
Georgios N DimitrakopoulosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0000-0002-7817-9552
Konstantinos LazarosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Panagiota GiannopoulouBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Aristidis G VrahatisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0000-0003-1892-0000
Marios G KrokidisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Panagiotis VlamosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0000-0003-0053-7847
Spyridon DoukakisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electroencephalography (EEG) provides a powerful means of capturing real-time neural activity, enabling the study of cognitive processes during complex learning tasks. This study explores the application of wearable EEG and advanced signal analysis to examine cognitive profiles of 30 sixth-grade students engaged in fraction learning. Using validated estimations alongside interactive digital tools such as Fraction Lab and the Diamond Paper task, EEG recordings were processed to evaluate spectral dynamics across delta, theta, alpha, and beta bands. Results revealed that lower-performing students exhibited elevated delta and theta power under cognitive load, whereas higher-performing students showed more stable beta activity linked to cognitive control. These findings highlight the utility of EEG-based signal analysis for identifying neurocognitive markers associated with conceptual and procedural knowledge (PK) in mathematics. The integration of such methodologies supports the development of precision-oriented educational strategies grounded in objective neural data. Clustering further revealed three learner profiles: Core Support Needed, Developing, and Advanced, while classification analyses confirmed that EEG features, especially gamma and beta oscillations, reliably distinguished among them, underscoring the potential of neurocognitive markers to guide adaptive instruction.

Indexed as

CognitionElectroencephalographyWearable Electronic DevicesChildFemaleHumansLearningMalecognitive profilingneurophysiological monitoringpersonalized educationsignal analysiswearable EEG

Identifiers

PMID41157499
PMCPMC12568116

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.