空间洞察：数据驱动的感兴趣区域选择如何增强基于 EEG 的 BCI 中的单次试验 P300 分类
Spatial Insight: How Data-Driven Regions of Interest Selection Enhances Single-Trial P300 Classification in EEG-Based BCIs
AI总结：
提出结合 eLORETA 源定位与跨被试功能连接的框架，通过识别空间稳定的感兴趣区域提升单次试验 P300 分类，在多频段优于全脑方法并助力阿尔茨海默病早期检测。
摘要：
基于 EEG 的脑机接口（BCI）在检测单次试验 P300 电位时经常面临空间特异性限制，而 P300 电位是用于 BCI 控制和神经退行性疾病诊断的神经生理学标志。我们提出了一个结合 eLORETA 源定位与跨被试功能连接的新框架，以识别跨会话稳定的感兴趣区域（ROI）。通过分析来自 31 名被试（63 个会话，2,520 次试验）的 62 通道 EEG 数据，我们证明相位滞后连接指标能够可靠地分离出位于更深层的皮层-皮层下结构（如脑岛和顶叶区域）中的任务相关枢纽，这些结构对阿尔茨海默病生物标志物至关重要。通过将空间稳定的 ROI 与动态时间一致性相结合，我们的混合分类系统在不同频段中系统地优于全脑方法（根据连接方法和频谱范围，提升最高达 5.4%），同时保持毫秒级的时间精度。据我们所知，这是首个通过源空间连接建立跨被试 ROI 共识的研究，绕过了头皮 EEG 的深度限制以探测与阿尔茨海默病相关的网络。该框架对噪声的鲁棒性及其与便携式系统的兼容性，为在全球范围内部署早期神经退行性疾病检测提供了巨大潜力。未来整合个体化解剖数据或自适应参数优化可改进该工具以用于临床部署，从而提升目前在 1-15 Hz 范围内 81.57% 的最高准确率。
英文摘要：
EEG-based Brain-Computer Interfaces (BCIs) frequently face spatial specificity limitations in detecting single-trial P300 potentials, a neurophysiological hallmark leveraged for both BCI control and neurodegenerative disease diagnostics. We present a novel framework combining eLORETA source localization with cross-subject functional connectivity to identify stable regions of interest (ROIs) across sessions. Analyzing 62-channel EEG data from 31 subjects (63 sessions, 2,520 trials), we demonstrate that phase-lagged connectivity metrics can reliably isolate task-relevant hubs in deeper cortical-subcortical structures like the insula and parietal regions - critical for Alzheimer's disease biomarkers. By integrating spatially stable ROIs with dynamic temporal agreement, our hybrid classification systematically outperforms whole-brain approaches in different frequency bands (up to 5.4% depending on the connectivity method and the spectral range) while maintaining millisecond-level temporal precision. To the best of our knowledge, this is the first study to establish cross-subject ROI consensus through source-space connectivity, bypassing scalp EEG's depth constraints to probe Alzheimer's-relevant networks. The framework's robustness to noise and compatibility with portable systems offer significant potential for global deployment in early neurodegenerative disease detection. Future integration of individualized anatomical data or adaptive parameter optimization could refine this tool for clinical deployment, enhancing the current max accuracy of 81.57% in the 1-15 Hz range.
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