# EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer

arXiv: https://arxiv.org/abs/2305.10502 · HTML: https://mlsanity.com/paper/2305.10502/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019)
- [Lite Transformer with Long-Short Range Attention](https://mlsanity.com/paper/2004.11886/) (2020)
- [Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View](https://mlsanity.com/paper/1906.02762/) (2019)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [Neural Memory Networks for Seizure Type Classification](https://mlsanity.com/paper/1912.04968/) (2019)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)

## Built on by (most cited first, 1 of 1)

- [Transformer-based EEG Decoding: A Survey](https://mlsanity.com/paper/2507.02320/) (2025)

## Abstract (arXiv)

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in EEG signal processing. Transformer models can capture the global dependencies in EEG signals through a self-attention mechanism, while CNN models can capture local features such as sawtooth waves. In this work, we propose an end-to-end neural epilepsy detection model, EENED, that combines CNN and Transformer. Specifically, by introducing the convolution module into the Transformer encoder, EENED can learn the time-dependent relationship of the patient's EEG signal features and notice local EEG abnormal mutations closely related to epilepsy, such as the appearance of spikes and the sprinkling of sharp and slow waves. Our proposed framework combines the ability of Transformer and CNN to capture different scale features of EEG signals and holds promise for improving the accuracy and reliability of epilepsy detection. Our source code will be released soon on GitHub.