neurokit2
NeuroKit2
Overview
NeuroKit2 provides a unified, high-level API for physiological signal processing. Each modality (ECG, EEG, EMG, EDA, PPG, RSP) follows the same nk.{signal}_process() → nk.{signal}_analyze() workflow: raw signal in, cleaned signal + features out. The library handles detrending, filtering, peak detection, artifact correction, and feature extraction automatically, with parameters tuned to biosignal characteristics. Results are returned as pandas DataFrames, making downstream statistics straightforward. NeuroKit2 also provides nk.{signal}_simulate() functions for generating synthetic test signals.
When to Use
- Extracting HRV (heart rate variability) features from ECG recordings for stress or autonomic nervous system analysis
- Detecting R-peaks in ECG and computing RR intervals, SDNN, RMSSD, pNN50, LF/HF ratio
- Processing EDA/GSR signals to separate tonic (SCL) and phasic (SCR) components for psychophysiology research
- Cleaning and segmenting EMG signals for muscle onset/offset detection in biomechanics
- Processing PPG signals from wearable sensors as ECG surrogates for heart rate and SpO2
- Generating synthetic physiological signals for algorithm validation and unit tests
- Use MNE instead when working with multichannel EEG/MEG and source localization; use scipy.signal for raw low-level DSP
Prerequisites
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