Segment cleaned EEG into event-locked epochs with baseline correction and artifact rejection.
npx skills add https://github.com/dengzhe-hou/auto-eeg-analysis --skill eeg-epoch
Please help me install this Agent Skill. Skill: eeg-epoch Source: https://github.com/dengzhe-hou/auto-eeg-analysis/tree/main/skills/eeg-epoch Command: npx skills add https://github.com/dengzhe-hou/auto-eeg-analysis --skill eeg-epoch
This Skill turns cleaned continuous EEG into event-locked epochs and removes bad trials so you can compute reliable ERPs/TFRs and downstream statistics.
Use eeg-epoch with your project directory to segment ICA-cleaned EEG into epochs around your event markers, rejecting bad trials and producing epoch-stage outputs ready for analysis.
High-intent search queries and answers about installing and using this skill.
To segment continuous EEG into event-locked epochs, the Skill extracts events from trigger channels, annotations, or BIDS sidecars, applies baseline correction, and performs trial-level artifact rejection to produce MNE Epoch FIF files ready for ERP analysis.
AutoReject works for trial-level artifact rejection by automatically evaluating EEG epochs to reject or interpolate artifact-contaminated trials, logging per-subject decisions to ensure clean data for downstream statistics without manual thresholding.
Yes, you need ICA-cleaned EEG data as the Skill requires existing ICA-stage outputs to segment continuous data into epochs and perform trial-level artifact rejection effectively.
Yes, you can use BIDS event sidecars to define condition codes for EEG epoching. The Skill extracts events from BIDS-style sidecars and maps them using condition codes defined in your DATASET_BRIEF.md file.
The best way to handle bad trials during EEG epoching is using AutoReject, which automatically rejects or interpolates artifact-contaminated trials and generates trial-count and exclusion reports to maintain reliable ERP results.
Baseline correction matters because it normalizes pre-stimulus EEG activity in event-locked epochs, applying a configurable pre-stimulus window to ensure accurate ERP measurement while safely skipping when inappropriate for your analysis design.
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