Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals.
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.
Apply this skill when working with:
Process electrocardiogram and photoplethysmography signals for cardiovascular analysis. See references/ecg_cardiac.md for detailed workflows.
Primary workflows:
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
Compute comprehensive HRV metrics from cardiac signals. See references/hrv.md for all indices and domain-specific analysis.
Supported domains:
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)
Analyze electroencephalography signals for frequency power, complexity, and microstate patterns. See references/eeg.md for detailed workflows and MNE integration.
Primary capabilities:
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)
Process skin conductance signals for autonomic nervous system assessment. See references/eda.md for detailed workflows.
Primary workflows:
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)
Analyze breathing patterns and respiratory variability. See references/rsp.md for detailed workflows.
Primary capabilities:
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)
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')
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)
Apply filtering, decomposition, and transformation operations to any signal. See references/signal_processing.md for comprehensive utilities.
Key operations:
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)
Compute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See references/complexity.md for all available metrics.
Available measures:
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)
Create epochs around stimulus events and analyze physiological responses. See references/epochs_events.md for workflows.
Primary capabilities:
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)
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)
NeuroKit2 automatically selects between two analysis modes based on data duration:
Event-related analysis (< 10 seconds):
Interval-related analysis (≥ 10 seconds):
Most *_analyze() functions automatically choose the appropriate mode.
pip install neurokit2
For development version:
pip install https://github.com/neuropsychology/NeuroKit/zipball/dev
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)
# 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)
# 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)
This skill includes comprehensive reference documentation organized by signal type and analysis method:
Load specific reference files as needed using the Read tool to access detailed function documentation and parameters.