# ScatterFormer: Locally-Invariant Scattering Transformer for Patient-Independent Multispectral Detection of Epileptiform Discharges

arXiv: https://arxiv.org/abs/2304.14919 · HTML: https://mlsanity.com/paper/2304.14919/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows](https://mlsanity.com/paper/2107.00652/) (2021)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021)
- [Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice](https://mlsanity.com/paper/2203.05962/) (2022)
- [Fast Vision Transformers with HiLo Attention](https://mlsanity.com/paper/2205.13213/) (2022)
- [Improving Vision Transformers by Revisiting High-frequency Components](https://mlsanity.com/paper/2204.00993/) (2022)
- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [Compressing the Input for CNNs with the First-Order Scattering Transform](https://mlsanity.com/paper/1809.10200/) (2018)
- [A Learnable ScatterNet: Locally Invariant Convolutional Layers](https://mlsanity.com/paper/1903.03137/) (2019)
- [Efficacy of Transformer Networks for Classification of Raw EEG Data](https://mlsanity.com/paper/2202.05170/) (2022)
- [Dual-Tree Wavelet Scattering Network with Parametric Log Transformation for Object Classification](https://mlsanity.com/paper/1702.03267/) (2017)
- [Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural Priors for Semantic Image Segmentation](https://mlsanity.com/paper/1802.03374/) (2018)

## Built on by (most cited first, 3 of 3)

- [Transformer-based EEG Decoding: A Survey](https://mlsanity.com/paper/2507.02320/) (2025)
- [Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals](https://mlsanity.com/paper/2305.04325/) (2023)
- [EEGDM: Learning EEG Representation with Latent Diffusion Model](https://mlsanity.com/paper/2508.20705/) (2025)

## Abstract (arXiv)

Patient-independent detection of epileptic activities based on visual spectral representation of continuous EEG (cEEG) has been widely used for diagnosing epilepsy. However, precise detection remains a considerable challenge due to subtle variabilities across subjects, channels and time points. Thus, capturing fine-grained, discriminative features of EEG patterns, which is associated with high-frequency textural information, is yet to be resolved. In this work, we propose Scattering Transformer (ScatterFormer), an invariant scattering transform-based hierarchical Transformer that specifically pays attention to subtle features. In particular, the disentangled frequency-aware attention (FAA) enables the Transformer to capture clinically informative high-frequency components, offering a novel clinical explainability based on visual encoding of multichannel EEG signals. Evaluations on two distinct tasks of epileptiform detection demonstrate the effectiveness our method. Our proposed model achieves median AUCROC and accuracy of 98.14%, 96.39% in patients with Rolandic epilepsy. On a neonatal seizure detection benchmark, it outperforms the state-of-the-art by 9% in terms of average AUCROC.