开源EEG工具可实时分析注意力负荷,支持从数据到应用的全流程落地。
NeuraDock Visual Cognitive Load Agent Tutorial: A Quality-Gated Open-Source EEG Workflow for Alpha Dynamics and Real-Time Applications

- 基于质量控制的流程,仅在通过筛选后才计算α波动态与认知负荷指标。
- 处理18段脑电数据,7/10对比中发现任务时后部α波抑制,验证了重复性。
- 适合需快速搭建实时认知负荷监测原型的研究者与开发者使用。
本教程提供NeuraDock Agent的逐步可复现操作指南,该开源EEG代理专注于α波动态与视觉认知负荷分析。目标是实用:读者可安装代理,完成预处理与质量控制,生成α波动态图,执行被试内静息/任务状态对比,运行公开小型数据集分析并与参考验证结果比较,启动在线仪表盘,从外部应用调用实时API,并利用大模型解释层理解质量风险。现有工具多支持离线分析,但构建实时、质量可控的认知负荷流水线常需手动整合采集、定制质控、α特征提取与网络接口;本教程填补此空白。采用质量门控工作流:下游α指标与负荷度量仅在通过预处理与质控后才计算。小型数据集验证中,代理处理18个记录,生成10次被试内对比,7/10对比观察到任务相关的后部α波抑制,初步估计被试内重复性,并基准化本地在线API延迟。适用于希望从脑电文件直达实时认知负荷原型的研究人员、开发人员及应用团队。
原文摘要 · Abstract (English)
This tutorial paper provides a step-by-step, reproducible walkthrough of NeuraDock Agent, an open-source EEG agent focused on Alpha dynamics and visual cognitive-load analysis. The goal is practical: a reader should be able to install the agent, run EEG preprocessing and quality control, generate Alpha dynamics figures, perform within-subject Rest/Task visual cognitive-load comparison, run the public mini-dataset analyses and compare them with the reference validation summary, start an online dashboard, call the real-time API from an external application, and use the LLM interpretation layer to explain quality risks. Existing EEG toolkits provide excellent offline analysis, but assembling a real-time, quality-gated cognitive-load pipeline often requires manually bridging acquisition, custom QC, Alpha feature extraction, and a web API; this tutorial closes that offline-to-online gap. The tutorial uses a quality-gated workflow: downstream Alpha and workload metrics are computed only after preprocessing and QC gating rather than directly from raw EEG. In the included mini-dataset validation, the agent processed 18 recordings, generated 10 within-subject comparisons, observed task-related posterior Alpha suppression in 7 of 10 contrasts, estimated initial evidence of within-subject repeatability, and benchmarked local online API latency. The tutorial is intended for researchers, developers, and applied teams who want a transparent path from EEG files to real-time visual cognitive-load prototypes.
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