Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
SKILL.md
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
uv pip install "neurokit2==0.2.13"
Installs
0
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:
signal identity and sensor/channel configuration;
native sampling rate in Hz and physical unit (or explicitly arbitrary_unit);
clock, timestamp origin, drift correction, and synchronization evidence;
polarity/orientation and acquisition-side filters/gain;
missing samples, discontinuities, saturation, flatlines, motion, and annotations;
whether event onsets are zero-based sample indices or seconds;
planned preprocessing order, methods, parameters, exclusions, and outputs; and
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.
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:
preserve immutable raw signal and annotations;
verify time base, units, polarity, clipping, gaps, and artifacts;
segment at long gaps; only interpolate short gaps under a declared policy;
apply modality-specific cleaning at the native sampling rate;
detect peaks/onsets or decompose components;
inspect quality outputs and raw overlays;
correct peaks only with logged categories and sensitivity checks;
derive rates/features;
align continuous modalities on a declared common time grid; and
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.
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.
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.
For neurokit/kim2004, amplitude_min is relative to the largest detected response;
it is not an absolute microsiemens threshold. cvxEDA needs optional cvxopt.
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:
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:
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.
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.
