The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • GIDP 2026
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2025.0161080
PDF

Hybrid Vision Transformer and MLP-Mixer for Epileptic Seizure Detection in Intracranial EEG

Author 1: Thouraya Guesmi
Author 2: Abir Hadriche
Author 3: Nawel Jmail

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 10, 2025.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Accurate and timely seizure detection is essential for effective epilepsy management, and automated systems can play a valuable role in supporting clinical practice. In this study, we introduce a hybrid approach that uses time-frequency representations of Intracranial electroencephalography (iEEG) signals filtered at High-Frequency Oscillations (HFOs) bands as input to different convolutional neural network (CNN) backbones for feature extraction, followed by classification with either a Vision Transformer (ViT) or MLP-Mixer. This work establishes a systematic, comparative framework for benchmarking hybrid CNN-ViT against CNN-MLP-Mixer, providing a critical new reference for automated epileptic seizure detection within HFOs filtered iEEG signals. Extensive evaluation demonstrates that the ViT consistently achieves superior performance, with an EfficientNetB0-ViT model attaining remarkable accuracy (97.85%) and specificity (98.92%). Crucially, the MLP-Mixer emerges as a highly competitive alternative, exhibiting strong recall capabilities that make it suitable for applications where missing a seizure is not an option. Overall, our findings suggest that self-attention mechanisms in ViTs provide a distinct advantage for capturing complex seizure dynamics, yet MLP-based models present a powerful, efficient option.

Keywords: Vision transformer; MLP-Mixer; iEEG; HFOs; ResNet; GoogleNet; EfficientNetB0

Thouraya Guesmi, Abir Hadriche and Nawel Jmail. “Hybrid Vision Transformer and MLP-Mixer for Epileptic Seizure Detection in Intracranial EEG”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.10 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161080

@article{Guesmi2025,
title = {Hybrid Vision Transformer and MLP-Mixer for Epileptic Seizure Detection in Intracranial EEG},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161080},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161080},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {10},
author = {Thouraya Guesmi and Abir Hadriche and Nawel Jmail}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org