Evidence map›Paper›PMID 39949891›Full record

ReviewInternational journal of telemedicine and applications2025

Threats and Mitigation Strategies for Electroencephalography-Based Person Authentication.

Zaib Unnisa, Asadullah Tariq, Irfan Ud Din, Danish Shehzad, Mohamed Adel Serhani, Abdelkader N Belkacem, Nadeem Sarwar

Abstract readReview
In one paragraph

Review in International journal of telemedicine and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  5. Review
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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

7 authors.

Zaib UnnisaDepartment of Computer Science, Superior University, Lahore, Pakistan.ORCID https://orcid.org/0000-0002-2533-2576
Asadullah TariqCollege of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi, UAE.ORCID https://orcid.org/0000-0002-2791-2347
Irfan Ud DinDepartment of Computer Science, New Uzbekistan University, Tashkent, Uzbekistan.ORCID https://orcid.org/0000-0002-7895-9911
Danish ShehzadDepartment of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan.ORCID https://orcid.org/0000-0002-4767-3443
Mohamed Adel SerhaniCollege of Computing and Informatics, University of Sharjah, Sharjah, UAE.ORCID https://orcid.org/0000-0001-7001-3710
Abdelkader N BelkacemCollege of Information Technology, United Arab Emirates University, Al Ain, Abu Dhabi, UAE.ORCID https://orcid.org/0000-0002-3024-4167
Nadeem SarwarDepartment of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan.ORCID https://orcid.org/0000-0001-8681-6382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work is aimed at investigating the potential risks linked to electroencephalography (EEG)-based person authentication and providing solutions to mitigate these issues. Authenticating a person by EEG involves verifying the legitimacy of the signals used for user identification. EEG signals serve as a biometric modality for authentication and verification. Additional biometric modalities, such as fingerprints or irises, are vulnerable to both fabrication and degradation over time, and illicit use of dead people's biometrics has sometimes been seen. EEG's intrinsic properties prohibit signal imitation or postmortem acquisition, making it more reliable than other biometric modalities. This research is aimed at investigating the most recent advancements in the domain of EEG signals, clarifying the current knowledge that impacts EEG-based authentication, and evaluating the emerging challenges. Many research publications have been collected to achieve this objective. By considering historical and recent efforts and achievements, this research also presents feasible resolutions to the emerging inquiries prompted by the ongoing advancements in EEG-based technology. The potential future application of EEG-based authentication has also been the subject of this scholarly discourse. A comprehensive collection of articles over the previous decade has been compiled to answer contemporary EEG signal research questions to get valuable insights. According to research findings, in February 2022, a significant milestone was achieved when the EEG signals of a deceased person were successfully captured for the first time in recorded history. However, this groundbreaking discovery may threaten EEG-based authentication. In addition, it is found that EEG-based authentication literature did not completely implement "liveness detection." An updated approach for identifying liveness addresses novel challenges, that is, falsified EEG signals and a dead person's EEG signals for EEG-based authentication that have not been discussed in the literature. The suggested solutions put forward in this study have the potential to stimulate additional research in this area.

Indexed as

authenticationEEG signalsEEG signals of a deceased personforged EEG signalsliveness detection

Identifiers

PMID39949891
PMCPMC11824856

What OpenQuestion holds

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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.