Plan EEGLAB preprocessing and analysis workflows for EEG data.
npx skills add https://github.com/MarvinCui/NeuroForge --skill eeglab
Please help me install this Agent Skill. Skill: eeglab Source: https://github.com/MarvinCui/NeuroForge/tree/main/NeuroForge/skills/eeglab Command: npx skills add https://github.com/MarvinCui/NeuroForge --skill eeglab
It helps you plan and route EEGLAB-focused EEG preprocessing and analysis tasks so you can structure correct, reproducible steps (including ICA, event/epoch handling, and QC) without blindly running heavy processing.
Use case: A user has raw EEG data and wants a step-by-step EEGLAB plan to prepare it for ERP analysis, including re-referencing, ICA-based artifact removal, and epoch extraction from specified event types.
Ask an AI assistant: “Using the eeglab skill, propose a safe EEGLAB preprocessing plan for continuous EEG targeting ERP analysis, and list the key EEGLAB functions and QC checks I should run before ICA and after artifact component removal.”
High-intent search queries and answers about installing and using this skill.
EEGLAB preprocessing for ERP analysis requires routing steps like channel handling, re-referencing, filtering, ICA preparation, and epoching in a safe, reproducible sequence. Planning ensures correct step ordering before running heavy processing.
ICA artifact removal is needed when preparing EEG data for analysis to separate neural signals from artifacts. It supports ICA decomposition planning and describes typical artifact-component classification and removal using common EEGLAB plugins.
Correct EEGLAB filtering and ICA step ordering involves filtering continuous data before ICA decomposition to ensure algorithm stability. Planning establishes this safe preprocessing sequence to prevent ICA artifacts from distorting subsequent event-related epoch extraction.
Mapping event types to epochs in EEGLAB involves extracting epochs from specified event types to prepare ERP or time-frequency analyses. Event-to-epoch workflow support translates user-provided event questions into a practical EEGLAB extraction plan.
QC checks before ICA and after artifact component removal verify data integrity and preprocessing effectiveness. Cautionary QC prompts guide user-provided EEG data validation to ensure safe, reproducible analysis pipelines without blind processing.
EEGLAB preprocessing planning applies to psychology and cognitive neuroscience pipelines requiring tool-specific routing and step ordering. It structures correct, reproducible workflows for continuous and event-related EEG data analysis tasks.
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