# Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges

arXiv: https://arxiv.org/abs/2503.19949 · HTML: https://mlsanity.com/paper/2503.19949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 266 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 22 of 22)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Multimodal Foundation Models: From Specialists to General-Purpose Assistants](https://mlsanity.com/paper/2309.10020/) (2023)
- [Concept-based Explainable Artificial Intelligence: A Survey](https://mlsanity.com/paper/2312.12936/) (2023)
- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024)
- [One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing](https://mlsanity.com/paper/1809.01926/) (2018)
- [Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment](https://mlsanity.com/paper/2412.17842/) (2024)
- [Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting](https://mlsanity.com/paper/2201.08780/) (2022)
- [Concept-based explainability for an EEG transformer model](https://mlsanity.com/paper/2307.12745/) (2023)
- [Deep Learning Approaches for Seizure Video Analysis: A Review](https://mlsanity.com/paper/2312.10930/) (2023)
- [Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures](https://mlsanity.com/paper/2205.03239/) (2022)
- [VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG](https://mlsanity.com/paper/2311.14775/) (2023)
- [EEG-based Multimodal Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2411.00822/) (2024)
- [Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures](https://mlsanity.com/paper/2007.00898/) (2020)
- [Generating Survival Interpretable Trajectories and Data](https://mlsanity.com/paper/2402.12331/) (2024)
- [Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning](https://mlsanity.com/paper/2402.14726/) (2024)
- [Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction](https://mlsanity.com/paper/2301.03465/) (2023)
- [EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units](https://mlsanity.com/paper/2301.10167/) (2022)
- [Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images](https://mlsanity.com/paper/2405.17483/) (2024)
- [Preictal Period Optimization for Deep Learning-Based Epileptic Seizure Prediction](https://mlsanity.com/paper/2407.14876/) (2024)
- [Seeing your sleep stage: cross-modal distillation from EEG to infrared video](https://mlsanity.com/paper/2208.05814/) (2022)
- [FI-CBL: A Probabilistic Method for Concept-Based Learning with Expert Rules](https://mlsanity.com/paper/2406.19897/) (2024)
- [RRAM-Based Bio-Inspired Circuits for Mobile Epileptic Correlation Extraction and Seizure Prediction](https://mlsanity.com/paper/2407.19841/) (2024)

## Abstract (arXiv)

Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitating automated tools for seizure detection. Recent advancements in machine learning have shown promise in real-time seizure detection and prediction using EEG and video data. However, diversity of seizure symptoms, markup ambiguities, and limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data. We also propose a novel pipeline for treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain.