西班牙 Neuroelectrics 无线高密度医用级脑电系统 Enobio 32

摘要 · 中文整理：生活在中低收入国家的儿童由于暴露于不利的生物学与环境危害，出现神经发育迟缓的风险显著更高。在婴幼儿期，贝利量表等整体发育评估工具敏感性不足，与后期认知结局的相关性不强，且需针对不同人群与文化背景进行适配。脑电图可客观测量脑电活动，有望提供预测长期认知结局的早期神经标志物；视觉诱发电位可考察视觉皮层加工效率并反映神经传导速度。移动脑电使得在以往难以开展此类技术的环境中评估神经加工成为可能。本文描述了作为纵向婴儿生长与发育（LIDG）研究（NCT06296238）一部分，在埃塞俄比亚阿姆哈拉农村地区实施移动脑电与视觉诱发电位的经验与教训。我们介绍了为优化数据采集（如改善头皮接触的干电极头、定制化流程与充分的设备配置）、应对环境挑战（如空间受限、缺乏供水、停电）以及本研究环境与人群特有的文化因素（如发质）所做的调整与策略。形成性研究强调了在社区成员（如母亲、父亲与宗教领袖）与当地临床人员中开展宣传以提升社区参与与认同的重要性；具有文化敏感性的儿童行为管理技巧对保证脑电完成率与高数据质量同样关键。经过社区宣教，脑电/视觉诱发电位的知情同意率高于 90%，平均记录时长为 20 ± 11 分钟。数据处理后，约 90% 与 70% 的受试分别达到静息态脑电与视觉诱发电位的预设数据质量阈值。结果表明，在埃塞俄比亚农村地区实施移动脑电/视觉诱发电位是可行且可接受的，且相当比例的记录达到了质量标准。

原文摘要：Children living in low- and middle-income countries (LMIC) are at disproportionately higher risk of neurodevelopmental delays due to exposure to adverse biological and environmental hazards. In infancy, global developmental assessments, such as the Bayley Scales, are insensitive, do not strongly correlate with later cognitive outcomes, and require adaptation for different populations and cultural contexts. Electroencephalography (EEG) objectively measures electrical brain activity and may provide early neural markers predictive of long-term cognitive outcomes. The visual evoked potential (VEP) interrogates the efficiency of visual cortical processing and reflects neural processing speed. Mobile EEG enables the assessment of neural processing in settings where such technologies were historically inaccessible. This paper describes the experiences and lessons learned from implementing mobile EEG and VEP in rural Amhara, Ethiopia as part of the Longitudinal Infant Growth and Development (LIDG) study (NCT06296238). We describe adaptations and strategies to address and optimize data capture (e.g., dry electrode tips to improve scalp contact, tailored protocols, and adequate equipment specifications), environmental challenges (e.g., space constraints, lack of water supply, power outage) and cultural factors (e.g., hair type) unique to the study setting and population. Our formative research underscored the importance of creating awareness among community members (e.g., mothers, fathers, and religious leaders) and local clinicians to improve community engagement and buy-in. Culturally sensitive child behavior management techniques were also critical to ensure EEG completion and high data quality. With community sensitization, we had high consent rates for EEG/VEP (>90%). We completed EEG recordings within an average ± standard deviation of 20 ± 11 minutes. After data processing, approximately 90% and 70% of participants met predefined data quality thresholds for resting EEG and VEP, respectively. Implementing mobile EEG/VEP was feasible and acceptable in rural Ethiopia, with a relatively high proportion of recordings meeting quality standards.

关键词：脑电图、神经发育、中低收入国家、营养、视觉诱发电位

研究目标：中低收入国家幼儿神经发育迟缓风险高，但现有全球性发育量表灵敏度不足、跨文化适用性差，而传统脑电设备难以在缺水、停电、场地受限的农村现场部署。论文要解决的问题是：移动脑电系统能否在埃塞俄比亚农村现场对幼儿稳定采集静息态脑电与图形翻转 VEP，并获得满足质量标准的数据。

主要结论：以 ENOBIO 32 移动脑电系统为核心的现场采集方案在埃塞俄比亚农村是可行且可被当地社区接受的：知情同意率超过 90%，单次记录平均 20±11 分钟，经 miniMADE 与 HAPPE 流程处理后约 90% 的静息态数据与约 70% 的 VEP 数据达到预设质量阈值。社区动员、文化敏感的儿童行为管理以及针对发质选用更长干电极针尖，是保障数据质量的关键因素。

创新点：系统总结了便携式 32 导移动脑电系统在资源受限农村现场对幼儿开展静息态与 VEP 采集的完整实施路径，将设备层面的适配（干电极针尖、电极帽贴合、参考/接地电极粘贴方案）、环境层面的应对（停电、缺水、空间受限）与文化层面的策略（社区动员、儿童行为管理）整合为可复制的实施框架，并给出了量化的可行性与数据质量指标。

后续建议：建立适用于浓密卷曲发质人群的标准化干电极接触阻抗评估与优化规程，进一步提升 VEP 数据达标率；延长纵向随访窗口，验证早期静息态脑电与 VEP 指标对后续认知结局的预测效力；引入现场实时数据质量反馈模块，使操作者在记录过程中即可发现坏导并及时处理；扩展至更多国家与文化背景的农村现场，检验该实施框架的可推广性；补充自动化的幼儿注视状态监测（如同步摄像或眼动），以客观剔除注视脱离的 VEP 试次

APA 引文：Chin, T. I., An, W. W., Yibeltal, K., Workneh, F., Pihl, S., Jensen, S. K. G., Asmamaw, G., Fasil, N., Teklehaimanot, A., North, K., Troller-Renfree, S. V., Nelson, C. A., Berhane, Y., & Lee, A. C. C. (2025). Implementation of mobile EEG for resting-state and visual evoked potentials in young children in rural Ethiopia. Frontiers in Human Neuroscience, 19, 1552410. https://doi.org/10.3389/fnhum.2025.1552410

仪器引用：The EEG assessment was conducted by two study nurses at local health centers using the Enobio-32 (Neuroelectrics, Barcelona, Spain), a 32-channel mobile EEG system. The Enobio-32 was selected for its portability, relatively lower cost, and previous validation in a large-scale, randomized controlled trial investigating the effects of poverty reduction on infant brain development.

摘要 · 中文整理：脑机接口（BCI）在将神经活动转换为外部辅助设备控制指令方面具有关键作用。脑电图（EEG）等非侵入式技术在捕捉与运动相关的脑信号时，能够在灵敏度与时空分辨率之间取得平衡。本文提出 MOVING——一个包含脑电信号与虚拟手套手部追踪数据的多模态数据集。该数据集包含与三种手部动作（握拳张开、手指叩击、腕关节旋转）以及静息状态相对应的脑电神经信号与运动学数据。数据采集自 11 名受试者，使用 32 导干电极无线脑电系统，并同步记录由配备两台正交 Leap Motion 控制器的虚拟手套（VG）系统采集的运动学数据。使用这两种设备可以实现快速装配（约 1 分钟），但相比数据采集金标准设备会引入更多噪声。研究考察了脑电信号中哪些频段对运动任务分类最具信息量，以及基线消减对手势识别的影响。研究采用深度学习方法（特别是 EEGnetV4）对基于脑电数据的动作进行分析与分类。该数据集旨在推动脑机接口研究以及面向手部运动障碍人群的辅助设备开发。本研究为持续扩充的脑电数据集库作出贡献，并有望成为新的脑机接口方法与应用的基准。

原文摘要：Brain–computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements—open/close, finger tapping, and wrist rotation—along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.

关键词：脑机接口、脑电图、虚拟手套、EEGnetV4 网络、数据集

研究目标：现有脑机接口研究缺乏同时包含高时间分辨率脑电信号与精确手部运动学数据、且可快速部署的公开多模态数据集；同时，哪些脑电频段对手部动作分类最具信息量、基线消减处理对识别性能有何影响，这些问题尚缺乏系统验证。论文旨在构建这样一个可快速装配、可公开复用的多模态数据集，并以此回答上述问题。

主要结论：使用 ENOBIO 32 干电极无线脑电系统配合双 Leap Motion 虚拟手套，可在约 1 分钟内完成装配并稳定同步采集 32 导脑电与 25 关节手部运动学数据；虽然干电极方案的噪声高于湿电极金标准设备，但基于 EEGNetV4 的深度学习模型仍能对握拳张开、手指叩击、腕关节旋转与静息四类状态进行有效区分，且特定频段与基线消减处理对手势识别性能有明显影响。所构建的 MOVING 数据集可作为脑机接口新方法的基准数据。

创新点：首次将快速装配的干电极无线脑电系统与由两台正交 Leap Motion 控制器组成的无接触虚拟手套相结合，构建了脑电与手部运动学严格时间同步的公开多模态数据集；同时在同一范式内同时采集运动想象与运动执行两种状态，并系统评估了频段选择与基线消减对分类性能的影响。

后续建议：扩大样本量并纳入左利手与手部运动障碍患者，提升数据集的代表性与临床迁移价值；对干电极引入的噪声建立专门的伪迹抑制与通道质量评估流程，缩小与湿电极金标准的性能差距；增加动作类别与连续动作序列，支持更贴近实际辅助控制需求的多自由度解码；引入在线实时解码验证，评估该多模态方案在闭环脑机接口中的可用性；补充肌电、眼动等额外模态，进一步区分运动想象与真实执行的神经特征

APA 引文：Mattei, E., Lozzi, D., Di Matteo, A., Cipriani, A., Manes, C., & Placidi, G. (2024). MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking. Sensors, 24(16), 5207. https://doi.org/10.3390/s24165207

仪器引用：The participant wears an Enobio® EEG system with 32 dry electrodes (P7, P4, Cz, Pz, P3, P8, O1, O2, T8, F8, C4, F4, Fp2, Fz, C3, F3, Fp1, T7, F7, Oz, PO4, FC6, FC2, AF4, CP6, CP2, CP1, CP5, FC1, FC5, AF3, PO3), while the VG device is placed under the right arm to record kinematic data.