Spatial Imputation Drives Cross-Domain Alignment for EEG Classification

TL;DR

This paper presents IMAC, a self-supervised framework that improves EEG classification across different domains by standardizing electrode layouts and using spatial imputation to align signals, achieving state-of-the-art results.

Contribution

IMAC introduces a novel spatial imputation approach with a channel-dependent mask and disentangled structure for robust cross-domain EEG classification.

Findings

Achieves up to 35% improvement in integrity scores.

Outperforms baseline methods in cross-subject and cross-center tasks.

Demonstrates robustness to distribution shifts.

Abstract

Electroencephalogram (EEG) signal classification faces significant challenges due to data distribution shifts caused by heterogeneous electrode configurations, acquisition protocols, and hardware discrepancies across domains. This paper introduces IMAC, a novel channel-dependent mask and imputation self-supervised framework that formulates the alignment of cross-domain EEG data shifts as a spatial time series imputation task. To address heterogeneous electrode configurations in cross-domain scenarios, IMAC first standardizes different electrode layouts using a 3D-to-2D positional unification mapping strategy, establishing unified spatial representations. Unlike previous mask-based self-supervised representation learning methods, IMAC introduces spatio-temporal signal alignment. This involves constructing a channel-dependent mask and reconstruction task framed as a low-to-high resolution EEG spatial imputation problem. Consequently, this approach simulates cross-domain variations such as channel omissions and temporal instabilities, thus enabling the model to leverage the proposed imputer for robust signal alignment during inference. Furthermore, IMAC incorporates a disentangled structure that separately models the temporal and spatial information of the EEG signals separately, reducing computational complexity while enhancing flexibility and adaptability. Comprehensive evaluations across 10 publicly available EEG datasets demonstrate IMAC's superior performance, achieving state-of-the-art classification accuracy in both cross-subject and cross-center validation scenarios. Notably, IMAC shows strong robustness under both simulated and real-world distribution shifts, surpassing baseline methods by up to $35$\% in integrity scores while maintaining consistent classification accuracy.