Simple Geometric Recentering Rivals Deep Sequence Models for Cross-Session EEG Motor-Imagery Decoding
A large and growing body of work applies increasingly complex deep architectures to EEG motor-imagery (MI) decoding, yet rarely tests whether that complexity is justified against a strong, simple geometric baseline under identical conditions. We report a controlled benchmark across eight public MI datasets (3–128 channels, 2–3 classes, single- and multi-session) that holds the feature representation fixed and varies only the decoder. The central method — a compact tangent-space pipeline on the SPD manifold with unsupervised test-time recentering, here called Geometry-Aware — is compared against three classical Riemannian baselines (TS+SVM, FgMDM, MDM) and a family of deep models built from our own prior architecture (a bidirectional Mamba mixture-of-experts, BiMamba+MoE, with two reduced ablation variants, and an SPDNet-style network), all consuming the same single-band covariance features. Across N = 88 subject-level observations cross-session and N = 120 within-session, Geometry-Aware achieves the best average rank cross-session and is statistically tied for the best within-session (second by raw rank but indistinguishable from TS+SVM under the critical-difference test). Its cross-session advantage is large and statistically decisive — it beats every competitor after multiple-comparison correction with large effect sizes (Cohen’s d = 1.06–1.50; all pFDR < 1.1 × 10−12) — yet within session its advantage over its recentering-free twin (TS+SVM) is statistically indistinguishable (d = − 0.00, p = 0.54). This cross/within double dissociation points to recentering as the operative mechanism rather than generic capacity. The deep sequence models (the Mamba variants), despite matched features and a fair, fixed training budget, underperform every classical Riemannian method in both protocols by wide margins; the SPDNet baseline fares better — beating MDM — but still never beats the simple tangent-space pipeline on identical features. We argue this is a positive, well-controlled result that directly answers the reviewer-style question of whether architectural complexity is warranted. We state the limitations — fairness of the deep-model comparison, the absence of a direct mechanistic probe, and dataset scope — and outline how each becomes a concrete next step.