CMEpiNet: Complex-Valued Multimodal Epilepsy Detection Network Model.

Overview

1. Department of Electrical and Information Engineering, Shandong University of Science and Technology, Jinan 250031, China
2. School of Physical Education and Health Linyi, Linyi University, Linyi 276012, China
3. School of Information Science and Engineering, Linyi University, Linyi 276012, China

Abstract

Existing seizure detection methods cannot fully exploit the spatiotemporal features of multimodal signals. They also fail to capture deep associations among cross-modal features. This limits their ability to learn unified representations of spatiotemporal dependencies. This work proposes CMEpiNet (Complex-valued Multimodal Epilepsy detection Network model) to address this issue. CMEpiNet first uses complex-valued convolutions for feature extraction. It explicitly models phase synchronization, phase shifts, and cross-frequency coupling. Thus, EEG, ECG, and EMG features are represented in the complex-valued domain. During feature fusion, CMEpiNet uses a two-level semantic alignment-based fusion method. It applies cross-modal consistency constraints in a shared alignment space. It also performs distribution-level alignment in an epilepsy-related semantic latent space. These operations ensure the consistency of multimodal features in the global semantic structure. Finally, CMEpiNet uses a spatial attention-guided 3D convolutional classifier. The classifier jointly models the temporal, feature, and modality dimensions. Experimental results on the SeizeIT2 dataset show that CMEpiNet improves seizure detection sensitivity, reduces the false alarm rate, and maintains stable performance under perturbations.

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:10.18112/openneuro.ds005873.v1.1.0, at OpenNeuro; found in “Data Availability Statement”

Data Availability Statement

The data presented in this study are openly available in OpenNeuro at https://doi.org/10.18112/openneuro.ds005873.v1.1.0, reference number ds005873.

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, 4 authors, 5 keywords, 9 MeSH terms, 3 funders, 36 references.

Cite

This paper

Su, T., Zhu, H., Chen, S., & Wang, H. (2026). CMEpiNet: Complex-Valued Multimodal Epilepsy Detection Network Model. Sensors (Basel, Switzerland), 26(13), 4186. https://doi.org/10.3390/s26134186

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1002/epi4.70260

A multicenter, video-EEG-based validation of a multimodal wearable device for focal seizure detection in adults: The SeizeIT2 study.

Journal: Epilepsia open

In common: OpenNeuro 10.18112/openneuro.ds005873.v1.1.0, epilepsy, other, EEG, 1 reference

[2] doi:10.1371/journal.pone.0345470

Physics-informed graph neural networks for robust cross-patient epileptic seizure prediction via chimera state detection.

Journal: PloS one

In common: epilepsy, EEG, 3 references

[3] doi:10.3390/bios16040203

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

Journal: Biosensors

In common: epilepsy, EEG, 2 references

[4] doi:10.3390/diagnostics16132012

Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.

Journal: Diagnostics (Basel, Switzerland)

In common: epilepsy, EEG, 2 references

[5] doi:10.1038/s41598-026-50003-5

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

Journal: Scientific reports

In common: epilepsy, other, EEG, 1 reference

[6] doi:10.3390/s26175623

MF-TopoNet: A Multi-Frequency Topological Neural Network for Epileptic Seizure Prediction.

Journal: Sensors (Basel, Switzerland)

In common: epilepsy, EEG, 1 reference

[7] doi:10.1038/s41598-026-55673-9

Patient-independent hybrid generative-discriminative modeling for seizure detection in long-term scalp EEG.

Journal: Scientific reports

In common: epilepsy, EEG, 1 reference

[8] doi:10.1186/s40708-026-00320-2

Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.

Journal: Brain informatics

In common: epilepsy, EEG, 1 reference

[9] doi:10.1038/s41598-026-48670-5

EEG-based harmful brain activity classification using deep learning and feature fusion.

Journal: Scientific reports

In common: epilepsy, EEG, 1 reference

[10] doi:10.1007/s40120-026-00924-0

Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.

Journal: Neurology and therapy

In common: epilepsy, EEG, 1 reference

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.

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: 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.