# Neurokit2

> Usa NeuroKit2 para construir o auditar flujos de investigación reproducibles: preprocesamiento de series temporales fisiológicas, análisis de eventos, alineación multimodal, variabilidad y complejidad. No es para diagnóstico.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/neurokit2
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/neurokit2.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: MIT
Actualizado: el mes pasado
Coste de contexto: 82 tok instalada, 3.3k tok al activarse, 45.1k tok con todos los archivos del bundle
Bundle: 20 archivos, 176 KB
Permisos que pide: read write edit bash glob

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill neurokit2 --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill neurokit2 --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill neurokit2 --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill neurokit2 --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill neurokit2 --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill neurokit2 --agent cline
```

## Qué hace

- Preprocesa señales fisiológicas (ECG, EDA, RSP, EMG, EOG, EEG, PPG) con limpieza, detección de picos y corrección
- Calcula HRV por dominio (tiempo, frecuencia, no lineal) y RSA a partir de ECG y RSP sincronizados
- Alinea múltiples modalidades en una malla temporal común y crea épocas con línea base alrededor de eventos
- Trata los esquemas de columnas devueltos como observaciones de runtime, no como listas fijas, y los persiste junto a la versión
- Incluye scripts acotados para generar señales sintéticas, inspeccionar datos, calcular HRV/EDA y validar manifiestos multimodales

## Cuándo usarla

- El código importa neurokit2 o necesita sus APIs, esquemas y validación actuales
- Se preprocesan series temporales fisiológicas o se calculan eventos, intervalos, variabilidad o complejidad
- Se necesita alinear señales multimodales (ECG, RSP, EDA) en una malla temporal común

## Cuándo no

- Presentar el resultado como diagnóstico, recomendación de tratamiento o decisión de monitorización de pacientes
- Usarlo como validación, certificación o evidencia regulatoria de un dispositivo médico
- Afirmar que un constructo fisiológico está validado en un sensor, protocolo o población nuevos

## Qué la activa

- "Procesa esta señal ECG con NeuroKit2 y calcula la HRV en el dominio de frecuencia"
- "Alinea ECG y RSP para calcular la RSA"
- "Genera una señal EDA sintética y detecta los picos SCR"

## Antes de instalar

- Requiere Python 3.10+ y uv; los flujos fijados usan NeuroKit2 0.2.13, con NumPy, SciPy, pandas, scikit-learn, matplotlib, PyWavelets y setuptools.
- Necesita en el PATH: python

## Archivos

- SKILL.md — 13 KB
- references/bio_module.md — 8 KB
- references/complexity.md — 7 KB
- references/ecg_cardiac.md — 7 KB
- references/eda.md — 7 KB
- references/eeg.md — 7 KB
- references/emg.md — 6 KB
- references/eog.md — 5 KB
- references/epochs_events.md — 7 KB
- references/hrv.md — 8 KB
- references/ppg.md — 7 KB
- references/rsp.md — 7 KB
- references/signal_processing.md — 7 KB
- scripts/_common.py — 19 KB
- scripts/ecg_hrv_pipeline.py — 11 KB
- scripts/eda_pipeline.py — 10 KB
- scripts/generate_synthetic.py — 7 KB
- scripts/inspect_signal.py — 13 KB
- scripts/plan_epochs.py — 10 KB
- scripts/validate_multimodal.py — 12 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT. Esta sección es el documento original y está en inglés.

# NeuroKit2

## Scope and evidence cutoff

Use this skill for method-aware, reproducible biosignal research with NeuroKit2. The
snapshot was checked on **2026-07-23** against:

- stable PyPI **0.2.13**, released 2026-03-02;
- Python metadata (`>=3.10`; classifiers 3.10–3.14) and wheel dependencies;
- GitHub release notes/tags, `NEWS.rst`, source at tag `v0.2.13`;
- official API pages/examples (the live site identified itself as
  `0.2.13.dev214`); and
- pinned 0.2.13 runtime signatures and synthetic output schemas.

The live documentation can be ahead of the stable wheel. Prefer the pinned runtime
for reproducible work and name both versions if consulting development docs.

## Boundary

NeuroKit2 is a research and educational toolbox. Do **not** present its output as:

- a diagnosis, treatment recommendation, patient-monitoring decision, or alarm;
- validation, certification, or regulatory evidence for a medical device; or
- proof that a physiological construct is measured validly in a new sensor,
  protocol, environment, population, or disease group.

Validate acquisition hardware, electrode/optode placement, units, sampling and clock
accuracy, preprocessing, detector/decomposition method, population, task, and
outcomes for the intended study. Preserve raw data and an auditable exclusion log.
Use deidentified local files only; do not place PHI in prompts, logs, examples, or
bundled fixtures.

## Reproducible installation

```bash
uv pip install "neurokit2==0.2.13"
```

For optional features, create a uv project, add only the packages actually required at
reviewed exact versions, and commit/review the resulting `uv.lock` before
`uv sync --locked`. NeuroKit2 exposes an upstream `full` extra, but this skill
intentionally does not install that floating transitive set in an automated workflow.
Optional capabilities can require MNE, cvxopt, Plotly, PyEMD, pyRQA, Pillow, OpenCV,
or file readers. Record the resolved environment with the analysis. Provision any MNE
data/template download as an explicit, checksummed study input. Do not install a moving
development branch for a reproducible study.

## Required data contract

Before processing, record:

1. signal identity and sensor/channel configuration;
2. native sampling rate in Hz and physical unit (or explicitly `arbitrary_unit`);
3. clock, timestamp origin, drift correction, and synchronization evidence;
4. polarity/orientation and acquisition-side filters/gain;
5. missing samples, discontinuities, saturation, flatlines, motion, and annotations;
6. whether event onsets are zero-based sample indices or seconds;
7. planned preprocessing order, methods, parameters, exclusions, and outputs; and
8. participant-level grouping needed to prevent leakage in later statistics.

Never infer units from a column name. Do not silently treat samples as milliseconds,
volts, microsiemens, or arbitrary units.

## Core workflow

### 1. Inspect before transforming

```bash
python skills/neurokit2/scripts/inspect_signal.py \
  --input recording.csv --root . --deidentified \
  --columns ECG,RSP,EDA --time-column time_s \
  --units ECG=mV,RSP=a.u.,EDA=uS
```

The inspector is bounded and emits no row values or paths. Resolve non-monotonic time,
duplicate samples, gaps, non-finite values, flat runs, and sampling-rate disagreement
before filtering.

### 2. Preserve preprocessing order

Use this default reasoning order, adapting it to the acquisition and cited method:

1. preserve immutable raw signal and annotations;
2. verify time base, units, polarity, clipping, gaps, and artifacts;
3. segment at long gaps; only interpolate short gaps under a declared policy;
4. apply modality-specific cleaning at the native sampling rate;
5. detect peaks/onsets or decompose components;
6. inspect quality outputs and raw overlays;
7. correct peaks only with logged categories and sensitivity checks;
8. derive rates/features;
9. align continuous modalities on a declared common time grid; and
10. map event indices to that grid, epoch, baseline, and analyze.

Do not resample binary markers or peak-index arrays as ordinary continuous signals.
Map their timestamps to the target grid. Filtering and interpolation can create edge
artifacts and false precision; retain masks for padded, missing, and rejected regions.

### 3. Treat schemas as runtime observations

Return columns depend on NeuroKit2 version, function, method, signal availability, and
analysis mode. Never claim that one column list is universal.

```python
signals, info = nk.ecg_process(ecg, sampling_rate=250)
observed_schema = {
    "columns": list(signals.columns),
    "info_keys": sorted(info),
}
```

Persist the observed schema with package version, method parameters, sampling rate, and
quality/exclusion summary. Reference files list verified default schemas for 0.2.13,
not guarantees for every method.

## Current patterns

### ECG, corrected peaks, and duration-aware HRV

In stable 0.2.13, `ecg_process()` performs cleaning, R-peak detection with
`correct_artifacts=True`, rate, default `averageQRS` quality, DWT delineation, and phase.

```python
signals, info = nk.ecg_process(ecg, sampling_rate=250, method="neurokit")
time_hrv = nk.hrv_time(info, sampling_rate=250)
```

Inspect `ECG_R_Peaks_Uncorrected` and `ECG_fixpeaks_*`; a corrected series is not
automatically a valid NN series. For frequency/nonlinear HRV, enforce metric-specific
duration and beat-count requirements. Five minutes is the conventional short-term
reference; ULF is a long-recording measure, and VLF interpretation from short records
is unsafe. Do not interpret LF/HF as a direct sympathovagal balance. PPG pulse-rate
variability is not interchangeable with ECG HRV.

Use the bounded pipeline:

```bash
python skills/neurokit2/scripts/ecg_hrv_pipeline.py \
  --synthetic --sampling-rate 250 --duration 300 \
  --domains time,frequency,nonlinear
```

### EDA with explicit decomposition

The stable default `eda_process(method="neurokit")` uses high-pass tonic/phasic
decomposition, not cvxEDA. Choose and report decomposition explicitly:

```python
clean = nk.eda_clean(eda, sampling_rate=100, method="neurokit")
components = nk.eda_phasic(clean, sampling_rate=100, method="highpass")
markers, info = nk.eda_peaks(
    components["EDA_Phasic"],
    sampling_rate=100,
    method="neurokit",
    amplitude_min=0.1,
)
```

For `neurokit`/`kim2004`, `amplitude_min` is relative to the largest detected response;
it is not an absolute microsiemens threshold. cvxEDA needs optional `cvxopt`.

```bash
python skills/neurokit2/scripts/eda_pipeline.py \
  --synthetic --sampling-rate 100 --duration 60 \
  --phasic-method highpass --peak-method neurokit
```

### Events, epochs, and baseline

`events_find()` reports zero-based sample onsets; duration/spacing arguments are in
samples. `epochs_create()` takes epoch limits in seconds.

```python
events = nk.events_find(trigger, threshold=0.5, duration_min=2)
epochs = nk.epochs_create(
    signals,
    events,
    sampling_rate=100,
    epochs_start=-0.2,
    epochs_end=0.8,
    baseline_correction=False,
)
```

Plan sample-exact windows first:

```bash
python skills/neurokit2/scripts/plan_epochs.py \
  --events 1000,2500,4000 --event-unit samples \
  --sampling-rate 100 --recording-samples 5000 \
  --epoch-start -0.2 --epoch-end 0.8 \
  --baseline-start -0.2 --baseline-end 0
```

In 0.2.13 the epoch slice is end-exclusive, but the generated floating time index
includes `epochs_end`. Built-in baseline correction subtracts the epoch mean from its
start through `t=0`; use manual correction for a narrower prespecified baseline.
Boundary epochs are padded and can contain NaN. Decide drop/pad/error before analysis.

### RSA and multimodal processing

`bio_process()` assumes all inputs already share one sampling rate and alignment. It
does not resample, synchronize, estimate drift, or create nested modality dictionaries;
its `info` output is flat. Unequal lengths are concatenated by index and can introduce
NaN. RSA is added only when synchronized ECG and RSP are present.

Validate a strict local manifest before calling it:

```bash
python skills/neurokit2/scripts/validate_multimodal.py \
  --manifest streams.json --root . --deidentified
```

After independent modality QC and alignment:

```python
bio_signals, bio_info = nk.bio_process(
    ecg=ecg_aligned,
    rsp=rsp_aligned,
    eda=eda_aligned,
    sampling_rate=common_rate,
)
rsa_summary = nk.hrv_rsa(
    bio_signals,
    bio_signals,
    rpeaks=bio_info,
    sampling_rate=common_rate,
    continuous=False,
)
```

Summary RSA is a dictionary; `continuous=True` returns a DataFrame with `RSA_P2T` and
`RSA_Gates` in the verified default workflow. Co-record respiration and report its
rate/depth/context; RSA is not a direct, context-free measure of vagal tone.

### Complexity returns values plus metadata

Most complexity functions in 0.2.13 return `(value, info)`. The convenience function
also returns two objects:

```python
features, details = nk.complexity(signal)  # default which="makowski2022"
sampen, sampen_info = nk.entropy_sample(signal)
dfa, dfa_info = nk.fractal_dfa(signal)
```

The default convenience selection is not “all measures.” Complexity estimates are
sensitive to length, stationarity, normalization, delay, dimension, tolerance, scale,
and implementation. Predefine them and run sensitivity/surrogate analyses.

## Bundled command-line helpers

All helpers reject URLs, path traversal, and symlinks; bound bytes/rows/channels; refuse
overwrite unless `--force`; use lazy scientific imports so `--help` works without
NeuroKit2; never use pickle; and produce deterministic JSON/CSV. Real-data commands
require `--deidentified`.

| Helper | Purpose |
|---|---|
| `scripts/generate_synthetic.py` | Dependency-free deterministic CSV fixtures |
| `scripts/inspect_signal.py` | Bounded CSV/time/gap/flatline inspection |
| `scripts/ecg_hrv_pipeline.py` | Pinned ECG, quality, peak-correction, HRV workflow |
| `scripts/eda_pipeline.py` | Explicit cleaning, decomposition, SCR workflow |
| `scripts/plan_epochs.py` | Sample-exact event, boundary, baseline planner |
| `scripts/validate_multimodal.py` | Strict units/rates/clocks/alignment schema validator |

Generate a fixture without exposing participant data:

```bash
python skills/neurokit2/scripts/generate_synthetic.py \
  --output synthetic.csv --root . --duration 30 \
  --sampling-rate 250 --seed 42
```

## Security note

No example or helper uses Python `eval()` or `exec()`. NeuroKit2 names such as
`eeg_*`, `events_*`, and `*_eventrelated()` are ordinary library calls. If a static
scanner reports an eval/exec pattern based on a substring, inspect the exact line and
record it as a scanner false positive only after confirming no dynamic execution exists.

## References

Read only the files needed for the modality or decision:
All bundled Markdown paths below are under `references/`; this skill has no
`templates/` or `assets/` reference paths.

| File | Contents |
|---|---|
| `references/signal_processing.md` | Filters, gaps, resampling, peaks, PSD, schemas |
| `references/epochs_events.md` | Event indexing, epoch boundaries, baselines |
| `references/ecg_cardiac.md` | ECG process, quality, delineation, peak correction |
| `references/hrv.md` | HRV/RSA inputs, duration, ectopy, interpretation |
| `references/eda.md` | Cleaning, decomposition, SCR detection |
| `references/emg.md` | EMG cleaning, amplitude, activation |
| `references/eog.md` | EOG polarity, MNE default, blink features |
| `references/eeg.md` | EEG/MNE helpers, power, QC, microstates |
| `references/ppg.md` | PPG methods, quality semantics, PRV limitations |
| `references/rsp.md` | Respiration polarity, rate, RRV/RVT/RAV |
| `references/bio_module.md` | Multimodal alignment and `bio_*` schemas |
| `references/complexity.md` | Tuple returns, parameter sensitivity, RQA |

## Primary sources checked 2026-07-23

- [PyPI 0.2.13](https://pypi.org/project/neurokit2/)
- [Official documentation](https://neuropsychology.github.io/NeuroKit/)
- [API index](https://neuropsychology.github.io/NeuroKit/functions/index.html)
- [GitHub releases](https://github.com/neuropsychology/NeuroKit/releases)
- [Makowski et al. (2021), NeuroKit2](https://doi.org/10.3758/s13428-020-01516-y)
- [Pham et al. (2021), HRV tutorial](https://doi.org/10.3390/s21123998)
- [Makowski et al. (2022), complexity comparison](https://doi.org/10.3390/e24081036)
- [SPR guideline index](https://sprweb.org/guidelines-papers)
- [Quigley et al. (2024), HR/HRV guidelines](https://doi.org/10.1111/psyp.14604)

## Dónde encaja

- Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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