# From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks

arXiv: https://arxiv.org/abs/2606.22258 · HTML: https://mlsanity.com/paper/2606.22258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 121 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [KAN or MLP: A Fairer Comparison](https://mlsanity.com/paper/2407.16674/) (2024)
- [A Survey on Kolmogorov-Arnold Network](https://mlsanity.com/paper/2411.06078/) (2024)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data](https://mlsanity.com/paper/2105.07536/) (2021)
- [CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis](https://mlsanity.com/paper/2409.04290/) (2024)
- [A Practitioner's Guide to Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2510.25781/) (2025)
- [SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms](https://mlsanity.com/paper/2402.13005/) (2024)
- [Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis](https://mlsanity.com/paper/2408.07314/) (2024)
- [Objective evaluation metrics for automatic classification of EEG events](https://mlsanity.com/paper/1712.10107/) (2017)
- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction](https://mlsanity.com/paper/2412.02211/) (2024)
- [Convolutional neural network for detection and classification of seizures in clinical data](https://mlsanity.com/paper/1903.08864/) (2019)
- [Gradient Boosting Machine: A Survey](https://mlsanity.com/paper/1908.06951/) (2019)
- [Interpretable Clinical Classification with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2509.16750/) (2025)
- [Bonn Activity Maps: Dataset Description](https://mlsanity.com/paper/1912.06354/) (2019)
- [CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation](https://mlsanity.com/paper/2412.15998/) (2024)
- [Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism](https://mlsanity.com/paper/2501.10342/) (2025)

## Abstract (arXiv)

Electroencephalogram (EEG) analysis remains the clinical gold standard for epilepsy diagnosis and seizure detection. While Deep Learning (DL) has significantly advanced automated EEG interpretation, its transition from controlled experimental settings to routine clinical deployment is severely bottlenecked by fundamental architectural flaws. Standard DL models operate as opaque black-boxes lacking clinical interpretability, demand massive amounts of balanced annotated data, and incur steep computational costs incompatible with resource-constrained wearable or implantable neuromodulation devices. This paper presents a comprehensive review of these prevailing limitations and explores Kolmogorov-Arnold Networks (KANs) as a emerging paradigm for EEG-based seizure detection. By replacing the fixed activation functions of traditional neurons with flexible, learnable functions along the network's connections, KANs bridge the critical gap between predictive accuracy and mathematical transparency. We systematically analyze how KAN architectures resolve the shortcomings of traditional DL-based models by offering exceptional parameter efficiency, inherent interpretability for physician trust, and robust performance under data scarcity. Ultimately, this review establishes KANs not merely as an incremental algorithmic update, but as a fundamental paradigm shift necessary to actualize next-generation, patient-specific, and thoroughly transparent clinical EEG monitoring systems.