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Agent skill

Erp Analysis

by NeuroAIHub in NeuroAIHub/BrainPilot

Domain-validated pipeline and parameter guidance for event-related potential analysis, from preprocessing through statistical testing

AGPL-3.0Auto-check passedData & Analytics

Install Erp Analysis

skills CLI
$ npx skills add NeuroAIHub/BrainPilot --skill erp-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NeuroAIHub/BrainPilot erp-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/skills/05_EEG_ERP/erp-analysis .claude/skills/erp-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
erp-analysis
GitHub stars
1.1k
Token cost
~2.8k tokens
SKILL.md length
1,318 words
Files
4 (incl. references)
Skills in repo
59
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Domain-validated pipeline and parameter guidance for event-related potential analysis, from preprocessing through statistical testing

  • Works in 5 steps: State the research question — What… → Justify the method choice — Why ERP (not… → Declare expected outcomes — Which… → …
  • Data & Analytics work in your project
  • SKILL.md covers Purpose, When to Use This Skill, Research Planning Protocol and ⚠️ Verification Notice, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Erp Analysis is an agent skill from NeuroAIHub/BrainPilot. Domain-validated pipeline and parameter guidance for event-related potential analysis, from preprocessing through statistical testing

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/erp-components.md`, `references/preprocessing-pipeline.md` and `references/statistical-approaches.md`).

It sits in Data & Analytics. The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.

When your agent uses it

  • Data & Analytics work in your project

Example prompts

  • “/erp-analysis”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. State the research question — What specific question is this ERP analysis addressing?
  2. Justify the method choice — Why ERP (not fMRI, behavior-only, etc.)? What alternatives were considered?
  3. Declare expected outcomes — Which component(s) do you expect to differ, in what direction?
  4. Note assumptions and limitations — What does this method assume? Where could it mislead?
  5. Present the plan to the user and WAIT for confirmation before proceeding.

What it can do on your machine

Read from SKILL.md and the folder at commit 93f6855. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Erp Analysis loads about 2.8k tokens when it runs, and up to ~23k if it reads all its reference files. Until then it costs about 37 tokens; SKILL.md has 1,318 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~23k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 1,318 words, ~2,785 tokens.

Download SKILL.mdSave it as .claude/skills/erp-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
erp-analysis
description
Domain-validated pipeline and parameter guidance for event-related potential analysis, from preprocessing through statistical testing
domain
cognitive-neuroscience
version
1.0.0
papers
Luck, 2014, Luck & Gaspelin, 2017, Keil et al., 2014, Kappenman & Luck, 2010
dependencies.required
research-literacy
review_status
ai-generated

ERP Data Analysis

Purpose

This skill encodes expert methodological knowledge for analyzing event-related potentials (ERPs) from EEG data. It provides domain-specific parameter recommendations, processing order guidance, component identification criteria, and statistical analysis strategies that a general-purpose programmer or data scientist would not know without specialized training.

When to Use This Skill

  • Designing an ERP preprocessing pipeline for a new study
  • Choosing filter settings, reference schemes, or artifact rejection criteria
  • Identifying which ERP component to measure and how to define its time window and ROI
  • Selecting appropriate amplitude measures (mean, peak, area) for a given component
  • Choosing between traditional ANOVA-based analysis and mass univariate approaches
  • Reviewing or troubleshooting an existing ERP analysis pipeline

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

  1. State the research question — What specific question is this ERP analysis addressing?
  2. Justify the method choice — Why ERP (not fMRI, behavior-only, etc.)? What alternatives were considered?
  3. Declare expected outcomes — Which component(s) do you expect to differ, in what direction?
  4. Note assumptions and limitations — What does this method assume? Where could it mislead?
  5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Preprocessing Pipeline Overview

The standard ERP preprocessing pipeline involves 7 ordered steps. Processing order matters and can influence results (the "multiverse" problem; see Lonedo et al., 2020).

  1. Import and inspect raw data -- Check for gross artifacts, disconnected electrodes
  2. Filter -- Apply bandpass and optional notch filter
  3. Re-reference -- Choose and apply reference scheme
  4. Bad channel identification and interpolation -- Identify and interpolate noisy channels
  5. ICA-based artifact correction -- Remove eye blinks, saccades, cardiac artifacts
  6. Epoching and baseline correction -- Segment continuous data around events
  7. Epoch rejection -- Remove remaining bad epochs by amplitude threshold

Multiverse note: Steps 2-5 interact. Filtering before ICA improves decomposition quality (Winkler et al., 2015). Some researchers re-reference after ICA. Document your choices and consider running key alternatives to assess robustness.

For detailed parameters at each step, see references/preprocessing-pipeline.md.

Key Parameter Defaults
StepParameterDefaultSource
FilterHigh-pass0.1 HzLuck, 2014, Ch. 5; Tanner et al., 2015
FilterLow-pass30 HzLuck, 2014, Ch. 5
FilterFilter typeFIR, zero-phaseWidmann et al., 2015
Re-referenceSchemeAverage referenceLuck, 2014, Ch. 5; Keil et al., 2014
Artifact rejectionThreshold+/-100 uVLuck, 2014
Bad channelsMax proportion< 10% of total channelsKeil et al., 2014
EpochingWindow-200 ms to 800 msLuck, 2014, Ch. 5
BaselineWindow-200 ms to 0 msLuck, 2014, Ch. 5

ERP Component Identification

To measure an ERP component correctly, you need three pieces of information:

  1. Latency range -- The expected time window for the component
  2. Scalp distribution -- Where on the scalp the component is maximal
  3. Functional context -- What experimental manipulation elicits the component
Component Selection Decision Logic
  1. Identify your cognitive process of interest (e.g., semantic processing, error monitoring)
  2. Look up the canonical component in references/erp-components.md
  3. Verify the component matches your paradigm -- The same voltage deflection in a different paradigm may reflect a different component (Luck, 2014, Ch. 2)
  4. Use the recommended ROI and time window as your starting point, then verify against your own grand average waveform
  5. For disputed components, read the Interpretation Notes in references/erp-components.md before committing to a theoretical interpretation
Component Domains at a Glance
DomainKey ComponentsReference File Section
Visual perceptionP1, N1, N170, N2pcerp-components.md Section 1
LanguageN400, P600/LPC, ELAN, LANerp-components.md Section 2
Executive controlERN/Ne, Pe, N2, CNVerp-components.md Section 3
MemoryFN400, parietal old/new, Dmerp-components.md Section 4

Statistical Analysis Strategy

Amplitude Measurement

Choose your measurement approach based on the component:

MeasureBest ForAvoid WhenSource
Mean amplitudeBroad components (N400, P300, LPC)Component is sharp and briefLuck, 2014, Ch. 9
Peak amplitudeSharp, well-defined peaks (P1, N1)Component has no clear peak or has multiple peaksLuck, 2014, Ch. 9
Peak latencyMeasuring processing speedComponent lacks a clear peakLuck, 2014, Ch. 9
50% fractional area latencyLatency with unequal amplitudes across conditionsRarely inappropriate; preferred over peak latencyLuck, 2014, Ch. 9; Kiesel et al., 2008
Signed/unsigned areaComponents spanning positive and negative voltagesSimple, unipolar componentsLuck, 2014, Ch. 9
Time Window and ROI Selection
  1. A priori selection (preferred): Choose time window and electrodes based on prior literature before looking at your data (Luck & Gaspelin, 2017)
  2. Collapsed localizer: Average across all conditions to identify the window/ROI, then test differences between conditions within that window (Luck & Gaspelin, 2017)
  3. Data-driven: Use mass univariate approach to avoid arbitrary window selection (see below)

Critical: Never select a time window or ROI by looking at the difference between conditions. This inflates Type I error (Luck & Gaspelin, 2017).

Show full SKILL.md (494 more words)Show less
Choosing a Statistical Framework
Is your hypothesis about a specific, well-characterized component?
 |
 +-- YES --> Do you have a priori time window and ROI?
 | |
 | +-- YES --> Traditional ANOVA on mean/peak amplitude
 | |
 | +-- NO --> Use collapsed localizer, then ANOVA
 |
 +-- NO --> Is your effect potentially distributed across time/space?
 |
 +-- YES --> Cluster-based permutation test (Maris & Oostenveld, 2007)
 |
 +-- NO --> Mass univariate with FDR correction (Groppe et al., 2011)

For detailed statistical method descriptions, see references/statistical-approaches.md.

Common Pitfalls

  1. Double-dipping: Selecting time windows or electrodes based on the effect of interest, then testing that same effect in the selected window (Kriegeskorte et al., 2009)
  2. Excessive filtering: High-pass cutoffs above 0.1 Hz can distort slow components like the P300, N400, and LPC (Tanner et al., 2015; Widmann & Schroger, 2012)
  3. Confounding component overlap: Apparent differences in one component may be driven by overlap from an adjacent component; consider difference waves and component-specific analyses (Luck, 2014, Ch. 2)
  4. Ignoring trial count imbalance: Unequal trial counts across conditions produce differential noise levels, biasing peak amplitude and latency measures (Luck, 2014, Ch. 9)
  5. Cluster-based tests for latency: Cluster permutation tests control family-wise error but cannot localize effects to specific time points or channels (Maris & Oostenveld, 2007)
  6. Reporting only p-values: Always report effect sizes (partial eta-squared for ANOVA, Cohen's d for t-tests) alongside p-values (Keil et al., 2014)

Minimum Reporting Checklist

Based on Keil et al. (2014) and Luck (2014):

  • Number of accepted trials per condition (minimum 30 per condition recommended; Boudewyn et al., 2018)
  • Filter settings (type, cutoff frequencies, roll-off)
  • Reference scheme
  • Artifact rejection method and criteria (ICA, threshold, proportion rejected)
  • Number and identity of interpolated channels
  • Epoch window and baseline correction window
  • Component time window and ROI electrodes (with justification)
  • Amplitude measure used (mean, peak, area)
  • Statistical test, correction method, and effect sizes

References

  • Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (2018). How many trials does it take to get a significant ERP effect? Psychophysiology, 55(6), e13049.
  • Groppe, D. M., Urbach, T. P., & Kutas, M. (2011). Mass univariate analysis of event-related brain potentials/fields I. Psychophysiology, 48(12), 1711-1725.
  • Keil, A., et al. (2014). Committee report: Publication guidelines and recommendations for studies using EEG and MEG. Psychophysiology, 51(1), 1-21.
  • Kiesel, A., Miller, J., Jolicoeur, P., & Brisson, B. (2008). Measurement of ERP latency differences. Psychophysiology, 45(4), 517-523.
  • Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S., & Baker, C. I. (2009). Circular analysis in systems neuroscience. Nature Neuroscience, 12(5), 535-540.
  • Lonedo, A., et al. (2020). The multiverse of ERP analysis pipelines. NeuroImage, 209, 116465.
  • Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique (2nd ed.). MIT Press.
  • Luck, S. J., & Gaspelin, N. (2017). How to get statistically significant effects in any ERP experiment (and why you shouldn't). Psychophysiology, 54(1), 146-157.
  • Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164(1), 177-190.
  • Tanner, D., Morgan-Short, K., & Luck, S. J. (2015). How inappropriate high-pass filters can produce artifactual effects. Psychophysiology, 52(8), 997-1009.
  • Widmann, A., Schroger, E., & Maess, B. (2015). Digital filter design for electrophysiological data. Journal of Neuroscience Methods, 250, 34-46.
  • Winkler, I., Debener, S., Muller, K. R., & Tangermann, M. (2015). On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. Proceedings of the IEEE EMBC, 4101-4105.

See references/ for detailed parameter tables, component database, and statistical method descriptions.

© NeuroAIHub, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in packages/skills/skills/05_EEG_ERP/erp-analysis of NeuroAIHub/BrainPilot.

  • SKILL.md
  • references/erp-components.md
  • references/preprocessing-pipeline.md
  • references/statistical-approaches.md

Open the folder on GitHubat commit 93f6855

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Categories

Questions about Erp Analysis

What does Erp Analysis do?

Domain-validated pipeline and parameter guidance for event-related potential analysis, from preprocessing through statistical testing. Erp Analysis is an agent skill from NeuroAIHub/BrainPilot.

When should I use Erp Analysis?

Erp Analysis fits situations like: data & Analytics work in your project.

How do I install Erp Analysis in Claude Code?

Run `npx skills add NeuroAIHub/BrainPilot --skill erp-analysis -a claude-code`. Or copy the skill folder (packages/skills/skills/05_EEG_ERP/erp-analysis in NeuroAIHub/BrainPilot) into .claude/skills/erp-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Erp Analysis in Codex?

Run `npx skills add NeuroAIHub/BrainPilot --skill erp-analysis -a codex`. Or copy the skill folder (packages/skills/skills/05_EEG_ERP/erp-analysis in NeuroAIHub/BrainPilot) into .agents/skills/erp-analysis in your project. Codex loads it when a task matches its description.

Can I use Erp Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NeuroAIHub/BrainPilot --skill erp-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/erp-analysis, .gemini/skills/erp-analysis, .github/skills/erp-analysis and .opencode/skills/erp-analysis in your project.

What does Erp Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Erp Analysis is instructions for the agent only.

Does Erp Analysis access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Erp Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Erp Analysis use?

Erp Analysis is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Erp Analysis use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 20k tokens, read only when the agent opens those files.

What are the alternatives to Erp Analysis?

Skills that share tags, products or a category with Erp Analysis: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Erp Analysis?

NeuroAIHub (a GitHub organization) maintains it in NeuroAIHub/BrainPilot, which has 1,062 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on October 2, 2026.

Source: NeuroAIHub/BrainPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.