Cross-domain multimodal learning for stress-level prediction: a hybrid deep learning framework integrating independent EEG and facial expression datasets.

Overview

Authors: Sukanya Pechetti¹, Lakshmi Naga Lasya Chennu¹, Vijay Chintakunta¹, Sumanth Dudala¹

ORCID iDs: Sukanya Pechetti

Department of Computer Science and Engineering, Siddhartha Academy of Higher Education, Deemed to be University, Vijayawada, Andhra Pradesh 520007 India

Institutions: Siddhartha Academy of Higher Education (India)

Journal: Scientific reports, volume 16, issue 1, article 15303

Dates: received 9 October 2025; accepted 18 February 2026; published online 1 April 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41598-026-41250-7 · PMID 41922398 · PMCID PMC13181068 · OpenAlex W7147690110

Open access: gold, a free copy (OpenAlex)

Status: data only

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

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

Keywords: Multimodal stress prediction, EEG signal analysis, Facial expression recognition, Vision transformer, Long short-term memory networks, Multimodal fusion, Computational biology and bioinformatics, Engineering, Mathematics and computing, Neuroscience

MeSH: Deep Learning*, Electroencephalography*, Facial Expression*, Stress, Psychological*, Convolutional Neural Networks, Humans, Long Short Term Memory (* major topic)

Topic: Emotion and Mood Recognition (Experimental and Cognitive Psychology, Psychology), according to OpenAlex

Citations: not cited yet (Europe PMC); 24 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

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

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Data

Datasets cited

kaggle.com/datasets/msambare, at Kaggle; 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: kaggle.com/datasets/msambare

Read it in the paper: doi.org/10.1038/s41598-026-41250-7.

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, 22 references.

Cite

This paper

Pechetti, S., Chennu, L. N. L., Chintakunta, V., & Dudala, S. (2026). Cross-domain multimodal learning for stress-level prediction: a hybrid deep learning framework integrating independent EEG and facial expression datasets. Scientific reports, 16(1), 15303. https://doi.org/10.1038/s41598-026-41250-7

BibTeX

@article{pechetti2026cross, author = {Pechetti, Sukanya and Chennu, Lakshmi Naga Lasya and Chintakunta, Vijay and Dudala, Sumanth}, title = {{Cross-domain multimodal learning for stress-level prediction: a hybrid deep learning framework integrating independent EEG and facial expression datasets}}, journal = {Scientific reports}, year = {2026}, month = apr, volume = {16}, number = {1}, pages = {15303}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-41250-7}, url = {https://doi.org/10.1038/s41598-026-41250-7}, pmid = {41922398}, pmcid = {PMC13181068} }

RIS

TY - JOUR AU - Pechetti, Sukanya AU - Chennu, Lakshmi Naga Lasya AU - Chintakunta, Vijay AU - Dudala, Sumanth TI - Cross-domain multimodal learning for stress-level prediction: a hybrid deep learning framework integrating independent EEG and facial expression datasets T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/04/01 VL - 16 IS - 1 SP - 15303 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-41250-7 UR - https://doi.org/10.1038/s41598-026-41250-7 LA - en ER -

CSL-JSON

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