Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection.

Overview

Authors: Jiale Chen¹, Weidong Zhou^1,2, Guoyang Liu^1,2

ORCID iDs: Jiale Chen, Weidong Zhou, Guoyang Liu

School of Integrated Circuits, Shandong University, Jinan 250101, China; (J.C.); (W.Z.)
Shenzhen Research Institute of Shandong University, Shenzhen 518000, China

Institutions: Shandong University (China)

Journal: Biosensors, volume 16, issue 4, article 203

Dates: received 6 February 2026; accepted 31 March 2026; published online 2 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/bios16040203 · PMID 42041424 · PMCID PMC13114204 · OpenAlex W7148460935

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: EEG (modality), human (organism), epilepsy (population)

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

Keywords: seizure detection, multiscale convolutional neural network, cosine convolution, Heterogeneous Two-Stream Network, EEG signal processing

MeSH: Electroencephalography*, Epilepsy*, Algorithms, Convolutional Neural Networks, Humans, Neural Networks, Computer, Signal Processing, Computer-Assisted (* major topic)

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

Funding: National Natural Science Foundation of China (62401342, 62271291); Natural Science Foundation of Shandong Province (ZR2024QF092, ZR2024LZH007, ZR2025ZD24); Guangdong Basic and Applied Basic Research Foundation (2025A1515011826, 2026A1515010768); Shenzhen Fundamental Research Program (JCYJ20250604124702003); National Key Research and Development Program of China (2024YFC2418300, 2024YFC2418303)

Citations: cited by 2 papers (Europe PMC); 53 references in the paper

Research resources: RRID:SCR_00734510

Abstract

The accurate detection of epileptic seizures using an electroencephalogram (EEG) is essential for clinical diagnosis and reducing the burden on clinicians but remains challenging due to low detection performance and model interpretability. In this study, we propose a Multiscale Cosine Convolutional Heterogeneous Two-Stream Cosine Convolution Network (MCC-HTSCC) to overcome these limitations. First, the raw EEG signals are input into the Multiscale Cosine Convolution (MCC) module, where multiscale temporal features are extracted by cosine convolutional layers with varying kernel lengths. Subsequently, the extracted temporal features are further processed through spatial convolutional layers to obtain comprehensive spatiotemporal representations. These spatiotemporal features are fused and subsequently fed into the Heterogeneous Two-Stream Cosine Convolution (HTSCC) module, comprising both deep and shallow subnetworks to perform hierarchical feature extraction and classification. Extensive evaluations were conducted on the publicly available CHB-MIT dataset and a clinically collected SH-SDU dataset, achieving accuracies of 98.52% and 94.56%, sensitivities of 97.98% and 88.09%, and specificities of 98.50% and 95.89%, respectively. Furthermore, the cosine convolution operators reduce the learnable parameters of our model by approximately 18.12% compared to the model with traditional convolution operators, making it more suitable for embedded deployment. By employing the Gradient-Weighted Class Activation Mapping (Grad-CAM) technique, we further provide interpretability and transparency in model decision making, highlighting the substantial potential of MCC-HTSCC for effective patient-specific epilepsy monitoring and diagnostics.

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

doi:10.13026/c2k01r, at the source; found in the references

Data Availability Statement

The data are available from the corresponding author upon reasonable request, subject to ethical and privacy restrictions.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 5 funders, 44 references, 1 RRID.

Cite

This paper

Chen, J., Zhou, W., & Liu, G. (2026). Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection. Biosensors, 16(4), 203. https://doi.org/10.3390/bios16040203

BibTeX

@article{chen2026multiscale, author = {Chen, Jiale and Zhou, Weidong and Liu, Guoyang}, title = {{Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection}}, journal = {Biosensors}, year = {2026}, month = apr, volume = {16}, number = {4}, pages = {203}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {2079-6374}, doi = {10.3390/bios16040203}, url = {https://doi.org/10.3390/bios16040203}, pmid = {42041424}, pmcid = {PMC13114204} }

RIS

TY - JOUR AU - Chen, Jiale AU - Zhou, Weidong AU - Liu, Guoyang TI - Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection T2 - Biosensors J2 - Biosensors (Basel) PY - 2026 DA - 2026/04/02 VL - 16 IS - 4 SP - 203 SN - 2079-6374 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/bios16040203 UR - https://doi.org/10.3390/bios16040203 LA - en ER -

CSL-JSON

{ "id": "10.3390/bios16040203", "type": "article-journal", "title": "Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection", "container-title": "Biosensors", "author": [ { "family": "Chen", "given": "Jiale" }, { "family": "Zhou", "given": "Weidong" }, { "family": "Liu", "given": "Guoyang" } ], "container-title-short": "Biosensors (Basel)", "volume": "16", "issue": "4", "page": "203", "DOI": "10.3390/bios16040203", "PMID": "42041424", "PMCID": "PMC13114204", "ISSN": "2079-6374", "publisher": "Multidisciplinary Digital Publishing Institute (MDPI)", "URL": "https://doi.org/10.3390/bios16040203", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 2 ] ] } }

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.