用脑电图识别抓握动作的计划阶段，发现低频振荡是关键信号。
Macroscopic EEG Reveals Discriminative Low-Frequency Oscillations in Plan-to-Grasp Visuomotor Tasks
- 分离抓握计划与执行阶段，用滤波银行空间模式提取特征。
- 0.5-8赫兹低频振荡在计划和执行中均实现75%以上分类准确率。
- 适合非侵入式脑机接口研究者参考，尤其关注动作意图解码。
基于视觉的抓握脑网络将视觉感知与认知及运动过程整合于视觉运动任务中。尽管侵入式记录已成功解码与抓握类型规划和执行相关的局部神经活动，但通过非侵入性脑电图（EEG）捕获的大规模神经激活模式仍了解甚少。我们引入一种新型视觉抓握平台，利用EEG神经影像研究在大范围脑网络中与抓握类型（精确抓、力量抓、无抓取）相关的神经活动。该平台在自然视觉运动任务中分离出抓握特异性规划阶段与其相关执行阶段，采用滤波银行共同空间模式（FBCSP）技术提取各阶段内具有区分性的频率特征。支持向量机（SVM）分类器用于二分类（精确抓对力量抓、有抓取对无抓取）及多分类（精确抓对力量抓对无抓取），并与传统运动相关皮层电位（MRCP）方法进行对比。结果显示，0.5–8赫兹的低频振荡在规划阶段建立并维持抓握相关信息，在两个阶段中对精确抓与力量抓的分类性能均稳定在75.3%–77.8%，优于MRCP的61.1%。高频活动（12–40赫兹）呈现阶段依赖性：抓取对无抓取分类达93.3%准确率，而精确抓对力量抓仅61.2%。基于SVM系数的特征重要性分析揭示，规划阶段前顶叶网络具有判别性特征，执行阶段则为运动网络。本研究证实低频振荡在非侵入性EEG中解码抓握类型规划阶段的有效性。
原文摘要 · Abstract (English)
The vision-based grasping brain network integrates visual perception with cognitive and motor processes for visuomotor tasks. While invasive recordings have successfully decoded localized neural activity related to grasp type planning and execution, macroscopic neural activation patterns captured by noninvasive electroencephalography (EEG) remain far less understood. We introduce a novel vision-based grasping platform to investigate grasp-type-specific (precision, power, no-grasp) neural activity across large-scale brain networks using EEG neuroimaging. The platform isolates grasp-specific planning from its associated execution phases in naturalistic visuomotor tasks, where the Filter-Bank Common Spatial Pattern (FBCSP) technique was designed to extract discriminative frequency-specific features within each phase. Support vector machine (SVM) classification discriminated binary (precision vs. power, grasp vs. no-grasp) and multiclass (precision vs. power vs. no-grasp) scenarios for each phase, and were compared against traditional Movement-Related Cortical Potential (MRCP) methods. Low-frequency oscillations (0.5-8 Hz) carry grasp-related information established during planning and maintained throughout execution, with consistent classification performance across both phases (75.3-77.8\%) for precision vs. power discrimination, compared to 61.1\% using MRCP. Higher-frequency activity (12-40 Hz) showed phase-dependent results with 93.3\% accuracy for grasp vs. no-grasp classification but 61.2\% for precision vs. power discrimination. Feature importance using SVM coefficients identified discriminative features within frontoparietal networks during planning and motor networks during execution. This work demonstrated the role of low-frequency oscillations in decoding grasp type during planning using noninvasive EEG.
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