Overcoming the BCI Calibration Bottleneck: A Clinically-Grounded Architecture using Riemannian Alignment and Stochastic Weight Averaging

• URL: http://arxiv.org/abs/2607.16225v1
• Date: Fri, 19 Jun 2026 22:18:39 GMT
• Title: Overcoming the BCI Calibration Bottleneck: A Clinically-Grounded Architecture using Riemannian Alignment and Stochastic Weight Averaging
• Abstract summary: Brain-Computer Interfaces (BCIs) face a severe calibration bottleneck due to cross-subject covariance shifts and physiological artifacts.<n>To enable zero-calibration BCI, a deep learning pipeline was engineered combining PerSession Independent Component Analysis, Euclidian Alignment, and EEGNet stabilized by Weight Averaging (SWA)<n>For the primary case study (Subject 1), a clinically robust SWA stable spatial accuracy of 90.97% (AUC: 0.976, Cohen's $$: 0.819) was achieved.<n> expanded 9-fold Leave-One-Subject-Out (LOSO) cross-validation yielded a globally
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain-Computer Interfaces (BCIs) face a severe calibration bottleneck due to cross-subject spatial covariance shifts and physiological artifacts. To enable zero-calibration BCI, a deep learning pipeline was engineered combining Per-Session Independent Component Analysis, Riemannian Euclidean Alignment, and EEGNet stabilized by Stochastic Weight Averaging (SWA). Evaluated on the strict MOABB BNCI2014-001 benchmark, the proposed architecture successfully isolates true sensorimotor rhythms. For the primary case study (Subject 1), a clinically robust SWA stable accuracy of 90.97% (AUC: 0.976, Cohen's $κ$: 0.819) was achieved. Furthermore, expanded 9-fold Leave-One-Subject-Out (LOSO) cross-validation yielded a globally stable mean accuracy of 74.31%, proving hardware-agnostic zero-shot efficacy for binary motor imagery.

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