NeuraDock Visual Cognitive Load Agent Tutorial: A Quality-Gated Open-Source EEG Workflow for Alpha Dynamics and Real-Time Applications

2026-06-24

AI Summary

This study addresses the lack of integrated workflows in existing EEG toolkits that seamlessly bridge offline analysis and real-time, quality-gated cognitive load assessment, which typically requires manual integration of signal acquisition, quality control, and feature extraction. The authors propose the first end-to-end, quality-gated real-time EEG cognitive load analysis pipeline, incorporating preprocessing, dynamic Alpha-band analysis, task-versus-rest comparisons, a low-latency web API, and an interpretability layer powered by a local large language model (LLM), complemented by an interactive dashboard. Evaluated on a small dataset of 18 recordings, the system successfully replicated 10 within-subject contrasts, with 7 showing expected posterior Alpha suppression during task conditions, thereby demonstrating both reproducibility and real-time performance while effectively bridging the gap between offline research and online applications.

Abstract

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.

Problem

• EEG
• cognitive load
• real-time
• Alpha dynamics
• quality control

Innovation

• quality-gated EEG
• Alpha dynamics
• real-time cognitive load
• open-source EEG workflow
• LLM interpretation