# Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

arXiv: https://arxiv.org/abs/2103.00606 · HTML: https://mlsanity.com/paper/2103.00606/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection](https://mlsanity.com/paper/1909.10868/) (2019)
- [ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification](https://mlsanity.com/paper/2006.07900/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Survey of Few-Shot Learning for Biomedical Time Series](https://mlsanity.com/paper/2405.02485/) (2024)
- [Intelligent and Miniaturized Neural Interfaces: An Emerging Era in Neurotechnology](https://mlsanity.com/paper/2405.10780/) (2024)
- [Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation](https://mlsanity.com/paper/2202.12598/) (2022)
- [Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG](https://mlsanity.com/paper/2301.03470/) (2023)

## Abstract (arXiv)

Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject. This subject-specific training scheme requires long labeled recordings from each patient, thus failing to detect symptoms in new patients with limited recordings. This paper introduces an unsupervised domain adaptation approach based on adversarial networks to enable few-shot, cross-subject epileptic seizure detection. Using adversarial learning, features from multiple patients were encoded into a subject-invariant space and a discriminative model was trained on subject-invariant features to make predictions. We evaluated this approach on the intracranial EEG (iEEG) recordings from 9 patients with epilepsy. Our approach enabled cross-subject seizure detection with a 9.4% improvement in 1-shot classification accuracy compared to the conventional subject-specific scheme.