EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network.

Overview

Authors: R. S. Soundariya¹, P. Thangaraj^1,2

Department of Computer Science and Engineering, Bannari Amman Institute of Technology,Sathyamangalam, Tamilnadu India
Department of Computer Science and Engineering, Kangeyam Institute of Technology, Kangeyam, India

Institutions: Bannari Amman Institute of Technology

Journal: Scientific reports, volume 16, issue 1, article 25441

Dates: received 24 October 2025; accepted 11 May 2026; published online 4 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41598-026-53295-9 · PMID 42243241 · PMCID PMC13473498 · OpenAlex W7163528233

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: EEG (modality), human (organism), cognitive (subfield)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Complexity, Physiology & signal measures

Keywords: Emotion recognition, EEG signal processing, Multi-scale wavelet transform, Kalman filtering, Reinforcement learning, Deep Q-Networks, Spatio-Temporal convolutional neural networks, Attention mechanism, Computational biology and bioinformatics, Engineering, Mathematics and computing, Neuroscience

MeSH: Electroencephalography*, Emotions*, Neural Networks, Computer*, Wavelet Analysis*, Algorithms, Graph Neural Networks, Humans, Signal Processing, Computer-Assisted (* major topic)

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

Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human–computer interaction, especially in adaptive systems. This paper proposes a novel emotion recognition system that improves classification accuracy through advanced signal processing, adaptive channel selection, and deep learning techniques. The system starts with the multi-scale wavelet transform to break down EEG signals effectively followed by Kalman filtering with Wavelet denoising that enhances the quality of signal. Emotional peaks are established using Spectral Entropy analysis for accurate epoch determination. For adaptive channel selection, the utilization of Reinforcement based Deep Q-Networks to pick the most informative EEG channels dynamically for each emotional state. In feature extraction, the hybrid model based on Spatio-Temporal Attention Networks (ST-ANs) is adopted to capture spatial and temporal dependencies in the EEG data. Multi-Scale Feature Fusion is utilized to fuse short- and long-term dependencies. Final emotion classification is done through an Ensemble of Graph Neural Networks (GNNs) and Memory-Augmented Neural Networks (MANNs) providing robust adaptability across different subjects and emotional states. The benchmark dataset is collected from kaggle repository such as EEG brainwave, DEAP dataset and Computer game based EEG dataset. The proposed work achieves 98.5% accuracy on the EEG Brainwave Dataset. On the DEAP Dataset, it achieves 94.5% accuracy. For the EDA on Emotion Recognition (S01G1AllChannels), the model reaches 97.6% accuracy, outperforming the existing frameworks in emotion classification.

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

Code

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

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

kaggle.com/datasets/samnikolas, at Kaggle; found in the references

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Reproduced under the paper's license (CC BY), 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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 12 keywords, 8 MeSH terms, 34 references.

Cite

This paper

Soundariya, R. S., & Thangaraj, P. (2026). EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network. Scientific reports, 16(1), 25441. https://doi.org/10.1038/s41598-026-53295-9

BibTeX

@article{soundariya2026eeg, author = {Soundariya, R. S. and Thangaraj, P.}, title = {{EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network}}, journal = {Scientific reports}, year = {2026}, month = jun, volume = {16}, number = {1}, pages = {25441}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-53295-9}, url = {https://doi.org/10.1038/s41598-026-53295-9}, pmid = {42243241}, pmcid = {PMC13473498} }

RIS

TY - JOUR AU - Soundariya, R. S. AU - Thangaraj, P. TI - EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/06/04 VL - 16 IS - 1 SP - 25441 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-53295-9 UR - https://doi.org/10.1038/s41598-026-53295-9 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41598-026-53295-9", "type": "article-journal", "title": "EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network", "container-title": "Scientific reports", "author": [ { "family": "Soundariya", "given": "R. S." }, { "family": "Thangaraj", "given": "P." } ], "container-title-short": "Sci Rep", "volume": "16", "issue": "1", "page": "25441", "DOI": "10.1038/s41598-026-53295-9", "PMID": "42243241", "PMCID": "PMC13473498", "ISSN": "2045-2322", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41598-026-53295-9", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 4 ] ] } }

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.