发表机构
University of Bern（伯恩大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对脑机接口校准瓶颈问题，构建结合逐会话独立成分分析、黎曼欧几里得对齐和随机权重平均稳定EEGNet的深度学习管道，在基准测试和案例研究中取得高准确率，验证了对二元运动想象的硬件无关零样本有效性。
AI 中文摘要
脑机接口（BCIs）由于跨受试者空间协方差偏移和生理伪迹而面临严重的校准瓶颈。为实现零校准BCI，设计了一种深度学习管道，结合了逐会话独立成分分析、黎曼欧几里得对齐和通过随机权重平均（SWA）稳定的EEGNet。在严格的MOABB BNCI2014 - 001基准上评估，该架构成功分离出真正的感觉运动节律。对于主要案例研究（受试者1），实现了临床稳健的SWA稳定准确率90.97%（AUC：0.976，科恩κ系数：0.819）。此外，扩展的9倍留一受试者出（LOSO）交叉验证产生了全局稳定的平均准确率74.31%，证明了对二元运动想象的硬件无关零样本有效性。
英文摘要
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.
Comments10 pages, 8 figures
