Adaptive Split-MMD Training for Small-Sample Cross-Dataset P300 EEG Classification

TL;DR

This paper introduces AS-MMD, a novel training method combining adaptive loss, split batch normalization, and MMD regularization, to improve small-sample cross-dataset P300 EEG classification.

Contribution

It proposes a new transfer learning approach for P300 EEG classification that effectively handles small samples and dataset shifts using adaptive and domain-specific techniques.

Findings

Outperforms target-only and pooled training methods in accuracy and AUC.

Significant improvements demonstrated across two public EEG datasets.

Ablation studies confirm the effectiveness of each component.

Abstract

Detecting single-trial P300 from EEG is difficult when only a few labeled trials are available. When attempting to boost a small target set with a large source dataset through transfer learning, cross-dataset shift arises. To address this challenge, we study transfer between two public visual-oddball ERP datasets using five shared electrodes (Fz, Pz, P3, P4, Oz) under a strict small-sample regime (target: 10 trials/subject; source: 80 trials/subject). We introduce Adaptive Split Maximum Mean Discrepancy Training (AS-MMD), which combines (i) a target-weighted loss with warm-up tied to the square root of the source/target size ratio, (ii) Split Batch Normalization (Split-BN) with shared affine parameters and per-domain running statistics, and (iii) a parameter-free logit-level Radial Basis Function kernel Maximum Mean Discrepancy (RBF-MMD) term using the median-bandwidth heuristic. Implemented on an EEG Conformer, AS-MMD is backbone-agnostic and leaves the inference-time model unchanged. Across both transfer directions, it outperforms target-only and pooled training (Active Visual Oddball: accuracy/AUC 0.66/0.74; ERP CORE P3: 0.61/0.65), with gains over pooling significant under corrected paired t-tests. Ablations attribute improvements to all three components.