Title: APR: Asymmetric Projector Regularization Improves EEG-to-Vision Contrastive Learning
Primary area: applications to neuroscience & cognitive science
Keywords: asymmetric projector, eeg, contrastive learning, vision

Abstract: Contrastive learning approaches that align electroencephalography (EEG) with visual representations have made progress in recent years, yet performance remains limited under the stringent 200-way zero-shot setting. We propose APR (Asymmetric Projector Regularization), a strategy that imposes asymmetric capacity on the dual-tower alignment. Concretely, the EEG branch uses a minimal single linear head followed by $ℓ2$ normalization, while the image branch retains a small, learnable projector (linear or a two-layer MLP) to accomplish the necessary geometric alignment. Built on NICE-style contrastive training with a frozen CLIP visual encoder, APR lifts zero-shot Top-1 accuracy from 13.8% to 30.45%, with concurrent gains in Top-5/Top-10, mean rank (mRank), and mean reciprocal rank (MRR). Systematic ablations show that simplifying the EEG-side projector improves generalization, whereas over-simplifying the image-side projector causes substantial performance degradation. APR remains robust across subjects, time windows, and temperature or batch-size settings. We advocate this small EEG head plus small, learnable image head recipe as a simple, reusable practice for more reliable cross-modal alignment in high-noise, low-sample EEG-to-vision scenarios. Code available at https://anonymous.4open.science/r/only-for-test-2026.