无需校准的脑机接口，跨被试稳定识别运动想象
Overcoming the BCI Calibration Bottleneck: A Clinically-Grounded Architecture using Riemannian Alignment and Stochastic Weight Averaging
- 用独立成分分析与黎曼对齐分离真实脑电信号
- 零校准下平均准确率达74.31%，单被试最高90.97%
- 适合临床部署，兼容不同硬件设备
脑-机接口因跨被试空间协方差偏移和生理伪影面临严重校准瓶颈。为实现零校准脑机接口，提出一种深度学习流水线，结合会话级独立成分分析、黎曼欧几里得对齐和经随机权重平均（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实的皮层运动节律。主案例研究（被试1）中，获得临床稳健的SWA稳定准确率90.97%（AUC: 0.976，Cohen's κ: 0.819）。此外，扩展9倍的留一被试交叉验证（LOSO）显示全局平均准确率为74.31%，证明其在二分类运动想象任务中具备硬件无关的零样本有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。