AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding

TL;DR

AFPM introduces a novel, calibration-free EEG decoding framework that aligns and models multi-dataset signals to improve cross-dataset BCI performance without dataset-specific tuning.

Contribution

The paper presents the first calibration-free cross-dataset EEG decoding method using alignment-based channel selection and spatiotemporal patch modeling.

Findings

Achieves up to 4.40% performance improvement on motor imagery tasks.

Achieves up to 3.58% performance improvement on ERP tasks.

Outperforms 17 state-of-the-art approaches in cross-dataset EEG decoding.

Abstract

Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-stationary signal distributions, and limited neurophysiological prior integration. To address these issues, we propose a plug-and-play Alignment-Based Frame-Patch Modeling (AFPM) framework, which has two main components: 1) Spatial Alignment, which selects task-relevant channels based on brain-region priors, aligns EEG distributions across domains, and remaps the selected channels to a unified layout; and, 2) Frame-Patch Encoding, which models multi-dataset signals into unified spatiotemporal patches for EEG decoding. Compared to 17 state-of-the-art approaches that need dataset-specific tuning, the proposed calibration-free AFPM achieves performance gains of up to 4.40% on motor imagery and 3.58% on event-related potential tasks. To our knowledge, this is the first calibration-free cross-dataset EEG decoding framework, substantially enhancing the practicalness of BCIs in real-world applications.