An ERP dataset for multi-information identity authentication.

Overview

Authors: Duan Li¹, Songyu Deng¹, Yimin Li¹, Zhifeng Zhang¹, Jiaofen Nan¹, Chunlai Yu², Liqin Yue², Chen Yang³, Hongxin Zhang³

ORCID iDs: Songyu Deng, Yimin Li, Chen Yang

School of Electronic Information and the Henan Key Laboratory of Information Functional Materials and Sensing Technology, Zhengzhou University of Light Industry, Zhengzhou, 450000, China
Faculty of Engineering, Huanghe Science and Technology College, Zhengzhou 450063, China
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

Institutions: Zhengzhou University of Light Industry (China); Huanghe Science and Technology College (China); Beijing University of Posts and Telecommunications (China)

Journal: Data in brief, volume 68, article 113148

Dates: received 8 May 2026; accepted 4 August 2026; published online 7 August 2026

Type: Data paper · Language: English

License: CC BY-NC

Identifiers: DOI 10.1016/j.dib.2026.113148 · PMID 42644107 · PMCID PMC13505431 · OpenAlex W7201884979

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: EEG (modality), methods / tools (subfield)

Methods: Spectral & time-frequency, Connectivity, Machine learning, Preprocessing, Evoked potentials, Statistics

Keywords: Electroencephalography, Event-related potentials, Rapid serial visual presentation, Identity authentication, Neural biometrics

Funding: Beijing University of Posts and Telecommunications (2025AI4S04); National Natural Science Foundation of China (62376035); Zhengzhou University of Light Industry (262102211027)

Citations: not cited yet (Europe PMC); 13 references in the paper

Abstract

Conventional biometric authentication methods, such as fingerprint recognition, facial recognition, and voiceprint recognition, are vulnerable to spoofing, coercion, and data leakage. Electroencephalography (EEG), owing to its resistance to replication and involuntary nature, has shown considerable potential in secure biometric authentication, particularly in brain–computer interface (BCI) systems based on rapid serial visual presentation (RSVP). However, authentication relying solely on facial information remains limited by the single-dimensional nature of the authentication cue, insufficient resistance to attacks, and limited robustness in practical applications. Therefore, this study constructs, for the first time, a BIDS-compliant RSVP-EEG dataset for multi-information identity authentication, incorporating three types of authentication factors: target faces, target names, and verification images. Data were collected from 32 healthy participants, each of whom completed two experimental sessions for all three authentication tasks with an interval of >24 h. Identity-related and identity-unrelated stimuli were presented at a frequency of 10 Hz to elicit prominent recognition-related neural responses, and EEG signals were recorded using an eight-channel portable EEG system covering frontal and occipital regions. The proposed experimental design integrates multiple types and hierarchical levels of identity-related information, providing a solid data foundation for ERP analysis, RSVP-based cognitive experimental research, and the development of multi-information identity authentication systems.

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

Code

The paper links to its data, not to its authors' code: see the Data section.

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Data

Datasets cited

doi:10.18112/openneuro.ds007541.v1.0.2, at OpenNeuro; found in the references
openneuro:ds007541, at OpenNeuro; found in the resources table

Data Availability

OpenNeuroMulti-Identity Information Authentication EEG Dataset (Original data) (https://openneuro.org/datasets/ds007541/versions/1.0.2).

Reproduced under the paper's license (CC BY-NC), 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 3, 28 September 2026

Authors: added Songyu Deng (0009-0003-1604-7663); Yimin Li (0009-0000-2915-4706); removed Songyu Deng; Yimin Li

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 9 authors, 5 keywords, 3 funders, 12 references.

Cite

This paper

Li, D., Deng, S., Li, Y., Zhang, Z., Nan, J., Yu, C., Yue, L., Yang, C., & Zhang, H. (2026). An ERP dataset for multi-information identity authentication. Data in brief, 68, 113148. https://doi.org/10.1016/j.dib.2026.113148

BibTeX

@article{li2026erp, author = {Li, Duan and Deng, Songyu and Li, Yimin and Zhang, Zhifeng and Nan, Jiaofen and Yu, Chunlai and Yue, Liqin and Yang, Chen and Zhang, Hongxin}, title = {{An ERP dataset for multi-information identity authentication}}, journal = {Data in brief}, year = {2026}, month = aug, volume = {68}, pages = {113148}, publisher = {Elsevier}, issn = {2352-3409}, doi = {10.1016/j.dib.2026.113148}, url = {https://doi.org/10.1016/j.dib.2026.113148}, pmid = {42644107}, pmcid = {PMC13505431} }

RIS

TY - JOUR AU - Li, Duan AU - Deng, Songyu AU - Li, Yimin AU - Zhang, Zhifeng AU - Nan, Jiaofen AU - Yu, Chunlai AU - Yue, Liqin AU - Yang, Chen AU - Zhang, Hongxin TI - An ERP dataset for multi-information identity authentication T2 - Data in brief J2 - Data Brief PY - 2026 DA - 2026/08/07 VL - 68 SP - 113148 SN - 2352-3409 PB - Elsevier DO - 10.1016/j.dib.2026.113148 UR - https://doi.org/10.1016/j.dib.2026.113148 LA - en ER -

CSL-JSON

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