EEG Abnormality Detection with NeuroGATE
Year: 2026
Type: software
Abstract: NeuroGATE v1.0 This is the first stable release of NeuroGATE, a deep learning framework for automated pathological EEG detection. This release contains the implementation used for the experiments reported in the associated study and is intended to support reproducibility and further research. Included in this release EEG preprocessing and data preparation pipelines NeuroGATE model implementation Training and evaluation scripts Support for experiments on TUAB and NMT Performance evaluation using standard classification metrics Ablation study experiments Statistical analysis utilities Saliency and model interpretation workflows Configuration and reproducibility resources Datasets The experiments in this repository use the TUAB and NMT datasets. Reproducibility The repository provides the code and experimental workflow required to reproduce the main NeuroGATE experiments. Dataset paths and environment specific settings should be configured locally before running the experiments. Version v1.0 represents the initial stable research release of NeuroGATE. Citation Citation information for the associated publication and archived software release will be added once the corresponding DOI and publication details are available.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214725356