Frequency-aware transformer networks for robust and generalizable EEG-based seizure detection.

Overview

1. Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518 Egypt
2. Artificial Intelligence Department, Faculty of Artificial Intelligence, Egyptian Russian University Badr City, Cairo, 11829 Egypt
3. Department of Neurosurgery, Faculty of Medicine, Benha University, Benha, 13518 Egypt

Abstract

Automated epileptic seizure detection from electroencephalogram (EEG) signals remains a critical challenge for real-world clinical deployment due to the complex, nonstationary, and multi-scale nature of neural dynamics. Existing deep learning approaches, including convolutional and transformer-based models, often fail to jointly capture spectral–temporal dependencies while maintaining robustness across heterogeneous datasets and noisy clinical environments. In this work, we propose BrainXNet, a novel multi-scale spectro-temporal attention framework that unifies local feature extraction, frequency-aware representation learning, and global temporal modeling within a single architecture. The proposed model integrates (i) multi-scale convolutional pathways to capture transient and long-duration EEG patterns, (ii) a spectral attention module that dynamically emphasizes clinically relevant frequency bands, and (iii) a temporal transformer encoder for modeling long-range dependencies across EEG sequences. Extensive evaluations on two large-scale benchmark datasets, CHB-MIT and TUH Seizure Corpus, demonstrate that BrainXNet achieves state-of-the-art performance, reaching accuracies of 99.1% and 98.4%, respectively. Beyond in-dataset performance, the proposed framework exhibits strong cross-dataset generalization, maintaining over 94% accuracy in transfer settings, and demonstrates high robustness under noisy conditions. Ablation studies further confirm the complementary contributions of each architectural component. These results highlight the effectiveness of explicitly modeling multi-scale spectro-temporal dynamics for EEG analysis and position BrainXNet as a promising candidate for reliable, real-time clinical seizure detection systems. This work bridges the gap between high-performance experimental models and practical deployment in diverse healthcare environments.

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

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Data

Datasets cited

• physionet.org/content/chbmit, at PhysioNet; found in “Data availability”

Data availability

The datasets analyzed during the current study are publicly available benchmark EEG datasets.The CHB-MIT Scalp EEG Database is publicly available through PhysioNet at: https://physionet.org/content/chbmit/1.0.0/The Temple University Hospital EEG Seizure Corpus (TUSZ) is publicly available through the official TUH EEG Corpus repository at: https://isip.piconepress.com/projects/nedc/html/tuh_eeg/index.shtml#c_tuszAccess to the TUH EEG Corpus requires registration and completion of a data use agreement through the repository website.No new datasets were generated during the current study.

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

Versions

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Version 3, 28 September 2026

• Funding: added Science and Technology Development Fund

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 9 keywords, 8 MeSH terms, 22 references.

Cite

This paper

Gamal, M., & Abdel-Wanes, M. (2026). Frequency-aware transformer networks for robust and generalizable EEG-based seizure detection. Scientific reports, 16(1), 28076. https://doi.org/10.1038/s41598-026-68506-6

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