Evidence map›Paper›PMID 34070732›Full record

ReviewSensors (Basel, Switzerland)2021

A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems.

Igor Stancin, Mario Cifrek, Alan Jovic

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing 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

54 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. [Fatigue driving detection based on prefrontal electroencephalogram asymptotic hierarchical fusion network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Wireless ear EEG to monitor drowsiness.Nature communications · 2024
    Article
  20. Towards predicting PTSD symptom severity using portable EEG-derived biomarkers.medRxiv : the preprint server for health sciences · 2024
    Article
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.

Igor StancinFaculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.
Mario CifrekFaculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.
Alan JovicFaculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.ORCID 0000-0003-3821-8091

Funding

European Regional Development Fund in the Republic of Croatia under the Operational Programme Competitiveness and Cohesion 2014 - 2020. KK.01.2.1.01.0136
6 · The paper itself

Abstract

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

Indexed as

ElectroencephalographyWakefulnessdeep learningdrowsiness classificationdrowsiness detectionEEG featuresfatigue detectionfeature extractionmachine learning

Identifiers

PMID34070732
PMCPMC8198610

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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.