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

TL;DR

This paper presents an unsupervised domain adaptation method using adversarial networks to improve cross-subject seizure detection in EEG data, enabling effective few-shot learning without extensive labeled data for new patients.

Contribution

The study introduces a novel adversarial learning framework that encodes features into a subject-invariant space for cross-subject seizure detection, outperforming traditional subject-specific models.

Findings

9.4% improvement in 1-shot classification accuracy

Effective encoding of subject-invariant features

Enables seizure detection with limited data

Abstract

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.