Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.

Overview

Authors: V S Arulmurugan¹, R Aarthy², S Sesha Vidhya³, Saranya K⁴

Department of Electrical and Electronics Engineering, Shree Venkateshwara Hi-Tech Engineering College (Autonomous), Gobichettipalayam, Erode, Tamil Nadu India
Computer Science and Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamil Nadu India
ECE, RMK College of Engineering and Technology, Puduvoyal, Tamil Nadu India
Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathya Mangalam, Tamil Nadu India

Institutions: Bannari Amman Institute of Technology

Journal: Scientific reports, volume 16, issue 1, article 20382

Dates: received 20 November 2025; accepted 17 April 2026; published online 2 May 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41598-026-50003-5 · PMID 42069762 · PMCID PMC13328674 · OpenAlex W7159985412

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: EEG (modality), other (modality), human (organism), epilepsy (population), methods / tools (subfield)

Methods: Spectral & time-frequency, Connectivity, Machine learning, Physiology & signal measures, Evoked potentials

Keywords: Federated learning, Seizure forecasting, Affective state analysis, EEG-ECG signal processing, Temporal convolutional networks, Privacy-preserving machine learning, Computational biology and bioinformatics, Engineering, Health care, Mathematics and computing

MeSH: Electroencephalography*, Seizures*, Convolutional Neural Networks, Federated Learning, Forecasting, Humans, Privacy (* major topic)

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

Citations: not cited yet (Europe PMC); 33 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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Data

Datasets cited

kaggle.com/datasets/raufmomin, at Kaggle; found in the references
physionet.org/content/chbmit, 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 a dataset: physionet.org/content/chbmit
it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41598-026-50003-5.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 10 keywords, 7 MeSH terms, 18 references.

Cite

This paper

Arulmurugan, V. S., Aarthy, R., Vidhya, S. S., & K, S. (2026). Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data. Scientific reports, 16(1), 20382. https://doi.org/10.1038/s41598-026-50003-5

BibTeX

@article{arulmurugan2026federated, author = {Arulmurugan, V S and Aarthy, R and Vidhya, S Sesha and K, Saranya}, title = {{Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data}}, journal = {Scientific reports}, year = {2026}, month = may, volume = {16}, number = {1}, pages = {20382}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-50003-5}, url = {https://doi.org/10.1038/s41598-026-50003-5}, pmid = {42069762}, pmcid = {PMC13328674} }

RIS

TY - JOUR AU - Arulmurugan, V S AU - Aarthy, R AU - Vidhya, S Sesha AU - K, Saranya TI - Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/05/02 VL - 16 IS - 1 SP - 20382 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-50003-5 UR - https://doi.org/10.1038/s41598-026-50003-5 LA - en ER -

CSL-JSON

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