phase-data-preprocess

Name: phase-data-preprocess
Availability: InStock
Author: stanislavjiricek

Filter, clean, epoch, and quality-check raw EEG, fMRI, ECG, and eye-tracking data.

6|Updated Mar 8, 2026

Search Tags:#quality control #neuroscience #data cleaning #data preprocessing #eeg #fmri

Ready to run

npx skills add https://github.com/stanislavjiricek/neuroflow --skill phase-data-preprocess

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: phase-data-preprocess
Source: https://github.com/stanislavjiricek/neuroflow/tree/main/skills/phase-data-preprocess
Command: npx skills add https://github.com/stanislavjiricek/neuroflow --skill phase-data-preprocess

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the complex process of filtering, cleaning, epoching, and quality-checking raw neuroscientific data, ensuring it's ready for accurate analysis.

Core Features & Use Cases

Modality-Specific Preprocessing: Adapts cleaning steps based on data type (EEG, fMRI, ECG, etc.).
Reproducible Workflows: Documents all parameter choices and logs deviations from preprocessing plans.
Quality Control: Generates essential QC plots and rejection summaries.
Use Case: Prepare raw EEG data for a cognitive experiment by applying band-pass filters, epoching around stimulus markers, and rejecting trials with excessive artifacts.

Quick Start

Run the data-preprocess skill to clean the raw EEG data located in the 'raw_eeg_data/' directory.

Frequently Asked Questions about phase-data-preprocess

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I preprocess raw EEG data for analysis?▼

Preprocess raw EEG data by applying modality-specific steps like band-pass filtering, epoching around stimulus markers, and rejecting trials with excessive artifacts. This ensures your dataset is analysis-ready and fully documented.

What steps are needed to clean fMRI and ECG data?▼

Cleaning fMRI and ECG data requires modality-specific preprocessing steps to filter noise and format signals. The process adapts cleaning steps based on the data type to ensure the datasets are ready for accurate analysis.

How do I perform quality control on neuroscientific data?▼

Perform quality control on neuroscientific data by generating essential QC plots and rejection summaries. This validates the preprocessing pipeline by documenting parameter choices and logging any deviations from the original plan.

Can I use this preprocessing approach for eye-tracking data?▼

Yes, this preprocessing approach handles eye-tracking data alongside EEG, fMRI, and ECG. It adapts the cleaning steps based on the specific data type to ensure all datasets are properly prepared for analysis.

How do I document parameter choices during data preprocessing?▼

Document parameter choices during data preprocessing by logging all settings and deviations from the preprocessing plan. This creates reproducible workflows and generates rejection summaries for quality control.

Why do I need to epoch raw data around stimulus markers?▼

Epoching raw data around stimulus markers segments continuous recordings into trial-based intervals for specific event analysis. This step is essential for preparing cognitive experiment datasets like EEG for accurate analysis.