SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG

TL;DR

This paper introduces SPDIM, a geometric deep learning framework for source-free unsupervised domain adaptation in EEG, effectively handling distribution and label shifts to improve generalization across subjects and days.

Contribution

SPDIM is a novel, parameter-efficient manifold optimization method that addresses label shifts in EEG domain adaptation, outperforming prior Riemannian alignment techniques.

Findings

SPDIM effectively compensates for distribution shifts in simulations.

SPDIM outperforms prior methods on EEG brain-computer interface datasets.

The approach improves EEG-based sleep staging accuracy.

Abstract

The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without labeled calibration data for target domains, the problem is a source-free unsupervised domain adaptation (SFUDA) problem. For scenarios with constant label distribution, Riemannian geometry-aware statistical alignment frameworks on the symmetric positive definite (SPD) manifold are considered state-of-the-art. However, many practical scenarios, including EEG-based sleep staging, exhibit label shifts. Here, we propose a geometric deep learning framework for SFUDA problems under specific distribution shifts, including label shifts. We introduce a novel, realistic generative model and show that prior Riemannian statistical alignment methods on the SPD manifold can compensate for specific marginal and conditional distribution shifts but hurt generalization under label shifts. As a remedy, we propose a parameter-efficient manifold optimization strategy termed SPDIM. SPDIM uses the information maximization principle to learn a single SPD-manifold-constrained parameter per target domain. In simulations, we demonstrate that SPDIM can compensate for the shifts under our generative model. Moreover, using public EEG-based brain-computer interface and sleep staging datasets, we show that SPDIM outperforms prior approaches.