Evaluating multi-level membership inference risk in federated EEG learning.

Overview

Authors: Taslima Khanam¹, Siuly Siuly¹, Kate Wang², Frank Whittaker³, Hua Wang¹

Institutions: Victoria University (Australia); RMIT University (Australia); Western Sydney University (Australia)

Journal: Brain informatics, volume 13, issue 1, article 26

Dates: received 8 January 2026; accepted 9 June 2026; published online 21 June 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1186/s40708-026-00313-1 · PMID 42323786 · PMCID PMC13328629 · OpenAlex W7165489995

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: EEG (modality)

Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics, Physiology & signal measures

Keywords: Federated learning, Electroencephalography, Brain–computer interface, Privacy preservation, Membership inference attack

Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Victoria University

Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Tracing map

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Data

Datasets cited

bbci.de/competition/iv, at bbci.de; found in “Data availability”
physionet.org/content/eegmmidb, at PhysioNet; found in “Data availability”

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to 2 datasets: bbci.de/competition/iv, physionet.org/content/eegmmidb

Read it in the paper: doi.org/10.1186/s40708-026-00313-1.

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 2, 28 September 2026

Funding: added Victoria University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 8 references.

Cite

This paper

Khanam, T., Siuly, S., Wang, K., Whittaker, F., & Wang, H. (2026). Evaluating multi-level membership inference risk in federated EEG learning. Brain informatics, 13(1), 26. https://doi.org/10.1186/s40708-026-00313-1

BibTeX

@article{khanam2026evaluating, author = {Khanam, Taslima and Siuly, Siuly and Wang, Kate and Whittaker, Frank and Wang, Hua}, title = {{Evaluating multi-level membership inference risk in federated EEG learning}}, journal = {Brain informatics}, year = {2026}, month = jun, volume = {13}, number = {1}, pages = {26}, publisher = {Springer}, issn = {2198-4018}, doi = {10.1186/s40708-026-00313-1}, url = {https://doi.org/10.1186/s40708-026-00313-1}, pmid = {42323786}, pmcid = {PMC13328629} }

RIS

TY - JOUR AU - Khanam, Taslima AU - Siuly, Siuly AU - Wang, Kate AU - Whittaker, Frank AU - Wang, Hua TI - Evaluating multi-level membership inference risk in federated EEG learning T2 - Brain informatics J2 - Brain Inform PY - 2026 DA - 2026/06/21 VL - 13 IS - 1 SP - 26 SN - 2198-4018 PB - Springer DO - 10.1186/s40708-026-00313-1 UR - https://doi.org/10.1186/s40708-026-00313-1 LA - en ER -

CSL-JSON

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