NeuraDock Visual Cognitive Load Agent Tutorial: A Quality-Gated Open-Source EEG Workflow for Alpha Dynamics and Real-Time Applications
Abstract
This tutorial provides a comprehensive guide for implementing an open-source EEG agent that analyzes Alpha dynamics and visual cognitive-load in real-time, covering preprocessing, quality control, feature extraction, and API integration for practical deployment.
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.
Community
This paper introduces NeuraDock Visual Cognitive Load Agent, a local-first EEG agent that turns EEG data into a quality-gated cognitive-load workflow with preprocessing, alpha-band analysis, dashboard visualization, and a local API.
The goal is not to claim “mind reading” or clinical diagnosis. Instead, we focus on making low-channel EEG workflows more reproducible, inspectable, and useful for developers, BCI/HCI researchers, and AI-agent builders.
Code is available here:
https://github.com/Neuradock/eeg-workstation-agent
The system can run without EEG hardware using synthetic replay / example data, and can also connect to NeuraDock EEG hardware for real-time experiments.
We are especially interested in feedback on:
signal-quality gating for low-channel EEG
cognitive-load / alpha-dynamics interpretation boundaries
local-first EEG agent design
browser / Cursor / VS Code integrations for cognitive-friction mapping
how EEG can serve as an optional calibration layer for human-AI workflow research
We are preparing Hugging Face datasets and dataset cards so the example / processed data can be loaded and explored more easily.
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