NeuroKit2

Overview

NeuroKit2 is a comprehensive Python toolkit for processing and analyzing physiological signals (biosignals). Use this skill to process cardiovascular, neural, autonomic, respiratory, and muscular signals for psychophysiology research, clinical applications, and human-computer interaction studies.

When to Use This Skill

Apply this skill when working with:

• Cardiac signals: ECG, PPG, heart rate variability (HRV), pulse analysis

• Brain signals: EEG frequency bands, microstates, complexity, source localization

• Autonomic signals: Electrodermal activity (EDA/GSR), skin conductance responses (SCR)

• Respiratory signals: Breathing rate, respiratory variability (RRV), volume per time

• Muscular signals: EMG amplitude, muscle activation detection

• Eye tracking: EOG, blink detection and analysis

• Multi-modal integration: Processing multiple physiological signals simultaneously

• Complexity analysis: Entropy measures, fractal dimensions, nonlinear dynamics

Core Capabilities

1. Cardiac Signal Processing (ECG/PPG)

Process electrocardiogram and photoplethysmography signals for cardiovascular analysis. See references/ecg_cardiac.md for detailed workflows.

Primary workflows:

• ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment

• HRV analysis across time, frequency, and nonlinear domains

• PPG pulse analysis and quality assessment

• ECG-derived respiration extraction

Key functions:

import neurokit2 as nk

Complete ECG processing pipeline

signals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)

Analyze ECG data (event-related or interval-related)

analysis = nk.ecg_analyze(signals, sampling_rate=1000)

Comprehensive HRV analysis

hrv = nk.hrv(peaks, sampling_rate=1000) # Time, frequency, nonlinear domains

2. Heart Rate Variability Analysis

Compute comprehensive HRV metrics from cardiac signals. See references/hrv.md for all indices and domain-specific analysis.

Supported domains:

• Time domain: SDNN, RMSSD, pNN50, SDSD, and derived metrics

• Frequency domain: ULF, VLF, LF, HF, VHF power and ratios

• Nonlinear domain: Poincaré plot (SD1/SD2), entropy measures, fractal dimensions

• Specialized: Respiratory sinus arrhythmia (RSA), recurrence quantification analysis (RQA)

Key functions:

All HRV indices at once

hrv_indices = nk.hrv(peaks, sampling_rate=1000)

Domain-specific analysis

hrv_time = nk.hrv_time(peaks) hrv_freq = nk.hrv_frequency(peaks, sampling_rate=1000) hrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=1000) hrv_rsa = nk.hrv_rsa(peaks, rsp_signal, sampling_rate=1000)

3. Brain Signal Analysis (EEG)

Analyze electroencephalography signals for frequency power, complexity, and microstate patterns. See references/eeg.md for detailed workflows and MNE integration.

Primary capabilities:

• Frequency band power analysis (Delta, Theta, Alpha, Beta, Gamma)

• Channel quality assessment and re-referencing

• Source localization (sLORETA, MNE)

• Microstate segmentation and transition dynamics

• Global field power and dissimilarity measures

Key functions:

Power analysis across frequency bands

power = nk.eeg_power(eeg_data, sampling_rate=250, channels=['Fz', 'Cz', 'Pz'])

Microstate analysis

microstates = nk.microstates_segment(eeg_data, n_microstates=4, method='kmod') static = nk.microstates_static(microstates) dynamic = nk.microstates_dynamic(microstates)

4. Electrodermal Activity (EDA)

Process skin conductance signals for autonomic nervous system assessment. See references/eda.md for detailed workflows.

Primary workflows:

• Signal decomposition into tonic and phasic components

• Skin conductance response (SCR) detection and analysis

• Sympathetic nervous system index calculation

• Autocorrelation and changepoint detection

Key functions:

Complete EDA processing

signals, info = nk.eda_process(eda_signal, sampling_rate=100)

Analyze EDA data

analysis = nk.eda_analyze(signals, sampling_rate=100)

Sympathetic nervous system activity

sympathetic = nk.eda_sympathetic(signals, sampling_rate=100)

5. Respiratory Signal Processing (RSP)

Analyze breathing patterns and respiratory variability. See references/rsp.md for detailed workflows.

Primary capabilities:

• Respiratory rate calculation and variability analysis

• Breathing amplitude and symmetry assessment

• Respiratory volume per time (fMRI applications)

• Respiratory amplitude variability (RAV)

Key functions:

Complete RSP processing

signals, info = nk.rsp_process(rsp_signal, sampling_rate=100)

Respiratory rate variability

rrv = nk.rsp_rrv(signals, sampling_rate=100)

Respiratory volume per time

rvt = nk.rsp_rvt(signals, sampling_rate=100)

6. Electromyography (EMG)

Process muscle activity signals for activation detection and amplitude analysis. See references/emg.md for workflows.

Key functions:

Complete EMG processing

signals, info = nk.emg_process(emg_signal, sampling_rate=1000)

Muscle activation detection

activation = nk.emg_activation(signals, sampling_rate=1000, method='threshold')

7. Electrooculography (EOG)

Analyze eye movement and blink patterns. See references/eog.md for workflows.

Key functions:

Complete EOG processing

signals, info = nk.eog_process(eog_signal, sampling_rate=500)

Extract blink features

features = nk.eog_features(signals, sampling_rate=500)

8. General Signal Processing

Apply filtering, decomposition, and transformation operations to any signal. See references/signal_processing.md for comprehensive utilities.

Key operations:

• Filtering (lowpass, highpass, bandpass, bandstop)

• Decomposition (EMD, SSA, wavelet)

• Peak detection and correction

• Power spectral density estimation

• Signal interpolation and resampling

• Autocorrelation and synchrony analysis

Key functions:

Filtering

filtered = nk.signal_filter(signal, sampling_rate=1000, lowcut=0.5, highcut=40)

Peak detection

peaks = nk.signal_findpeaks(signal)

Power spectral density

psd = nk.signal_psd(signal, sampling_rate=1000)

9. Complexity and Entropy Analysis

Compute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See references/complexity.md for all available metrics.

Available measures:

• Entropy: Shannon, approximate, sample, permutation, spectral, fuzzy, multiscale

• Fractal dimensions: Katz, Higuchi, Petrosian, Sevcik, correlation dimension

• Nonlinear dynamics: Lyapunov exponents, Lempel-Ziv complexity, recurrence quantification

• DFA: Detrended fluctuation analysis, multifractal DFA

• Information theory: Fisher information, mutual information

Key functions:

Multiple complexity metrics at once

complexity_indices = nk.complexity(signal, sampling_rate=1000)

Specific measures

apen = nk.entropy_approximate(signal) dfa = nk.fractal_dfa(signal) lyap = nk.complexity_lyapunov(signal, sampling_rate=1000)

10. Event-Related Analysis

Create epochs around stimulus events and analyze physiological responses. See references/epochs_events.md for workflows.

Primary capabilities:

• Epoch creation from event markers

• Event-related averaging and visualization

• Baseline correction options

• Grand average computation with confidence intervals

Key functions:

Find events in signal

events = nk.events_find(trigger_signal, threshold=0.5)

Create epochs around events

epochs = nk.epochs_create(signals, events, sampling_rate=1000, epochs_start=-0.5, epochs_end=2.0)

Average across epochs

grand_average = nk.epochs_average(epochs)

11. Multi-Signal Integration

Process multiple physiological signals simultaneously with unified output. See references/bio_module.md for integration workflows.

Key functions:

Process multiple signals at once

bio_signals, bio_info = nk.bio_process( ecg=ecg_signal, rsp=rsp_signal, eda=eda_signal, emg=emg_signal, sampling_rate=1000 )

Analyze all processed signals

bio_analysis = nk.bio_analyze(bio_signals, sampling_rate=1000)

Analysis Modes

NeuroKit2 automatically selects between two analysis modes based on data duration:

Event-related analysis (< 10 seconds):

• Analyzes stimulus-locked responses

• Epoch-based segmentation

• Suitable for experimental paradigms with discrete trials

Interval-related analysis (≥ 10 seconds):

• Characterizes physiological patterns over extended periods

• Resting state or continuous activities

• Suitable for baseline measurements and long-term monitoring

Most *_analyze() functions automatically choose the appropriate mode.

Installation

pip install neurokit2

For development version:

pip install https://github.com/neuropsychology/NeuroKit/zipball/dev

Common Workflows

Quick Start: ECG Analysis

import neurokit2 as nk

Load example data

ecg = nk.ecg_simulate(duration=60, sampling_rate=1000)

Process ECG

signals, info = nk.ecg_process(ecg, sampling_rate=1000)

Analyze HRV

hrv = nk.hrv(info['ECG_R_Peaks'], sampling_rate=1000)

Visualize

nk.ecg_plot(signals, info)

Multi-Modal Analysis

Process multiple signals

bio_signals, bio_info = nk.bio_process( ecg=ecg_signal, rsp=rsp_signal, eda=eda_signal, sampling_rate=1000 )

Analyze all signals

results = nk.bio_analyze(bio_signals, sampling_rate=1000)

Event-Related Potential

Find events

events = nk.events_find(trigger_channel, threshold=0.5)

Create epochs

epochs = nk.epochs_create(processed_signals, events, sampling_rate=1000, epochs_start=-0.5, epochs_end=2.0)

Event-related analysis for each signal type

ecg_epochs = nk.ecg_eventrelated(epochs) eda_epochs = nk.eda_eventrelated(epochs)

References

This skill includes comprehensive reference documentation organized by signal type and analysis method:

• ecg_cardiac.md: ECG/PPG processing, R-peak detection, delineation, quality assessment

• hrv.md: Heart rate variability indices across all domains

• eeg.md: EEG analysis, frequency bands, microstates, source localization

• eda.md: Electrodermal activity processing and SCR analysis

• rsp.md: Respiratory signal processing and variability

• ppg.md: Photoplethysmography signal analysis

• emg.md: Electromyography processing and activation detection

• eog.md: Electrooculography and blink analysis

• signal_processing.md: General signal utilities and transformations

• complexity.md: Entropy, fractal, and nonlinear measures

• epochs_events.md: Event-related analysis and epoch creation

• bio_module.md: Multi-signal integration workflows

Load specific reference files as needed using the Read tool to access detailed function documentation and parameters.

Additional Resources

• Official Documentation: https://neuropsychology.github.io/NeuroKit/

• GitHub Repository: https://github.com/neuropsychology/NeuroKit

• Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https://doi.org/10.3758/s13428-020-01516-y