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

• URL: http://arxiv.org/abs/2510.21969v1
• Date: Fri, 24 Oct 2025 18:48:21 GMT
• Title: Adaptive Split-MMD Training for Small-Sample Cross-Dataset P300 EEG Classification
• Authors: Weiyu Chen, Arnaud Delorme,
• Abstract summary: Cross-dataset shift occurs when trying to boost a small target set with a large source dataset.<n>We introduce Adaptive Split Maximum Mean Discrepancy Training (AS-MMD)<n>AS-MMD combines a target-weighted loss with warm-up tied to the square root of the source/target size ratio.<n>It outperforms target-only and pooled training.
• Score: 12.103074826558531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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