EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network

The EPIC-NET model improves the overall accuracy by 5.92, 10.02, and 0.59% better than RNN, SVM and CNN, respectively.

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Publication Details

Journal
Frontiers in Computational Neuroscience
Published
2026-02-03
DOI
https://doi.org/10.3389/fncom.2025.1725924
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00

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article

EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network

Ravichandran Manjupriya, A. Anny Leema
Frontiers in Computational Neuroscience
EEG and Brain-Computer Interfaces
article

EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network

Ravichandran Manjupriya, A. Anny Leema
article en

Abstract

The EPIC-NET model improves the overall accuracy by 5.92, 10.02, and 0.59% better than RNN, SVM and CNN, respectively.

Frontiers in Computational NeuroscienceVol. 19
Vellore Institute of Technology University (IN)
VIT University
Openalex Percentile: Top 69%
EEG and Brain-Computer Interfaces
DOIOpen Access PDFOpenAlex
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EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network — Ravichandran Manjupriya, A. Anny Leema · Frontiers in Computational Neuroscience (2026) | TGRS Research Map | TGRS