Process and analyze neurophysiological signals including EDA (electrodermal activity), ECG (electrocardiogram), and RSP (respiration). Use when extracting skin conductance responses, detecting heartbeat R-peaks, analyzing breathing patterns, or computing biosignal features from CSV time-series data.
NeuroKit2 is a Python toolkit for processing biosignals. It provides automated pipelines for cleaning, peak detection, and feature extraction from EDA, ECG, EMG, RSP, and other physiological signals.
python scripts/extract_eda_scr.py --input eda_signal.csv --output scr_features.csv --sampling_rate 250
scripts/extract_eda_scr.pyExtract Skin Conductance Response (SCR) features from EDA signals.
python scripts/extract_eda_scr.py --input <eda.csv> --output <features.csv> --sampling_rate <hz>
Parameters:
--input — CSV file with single-column EDA signal data--output — Output CSV with SCR onset, peak, amplitude, rise time--sampling_rate — Signal sampling rate in Hz (must match recording device)scripts/extract_ecg_peaks.pyDetect R-peaks and P-waves from ECG signals.
python scripts/extract_ecg_peaks.py --input <ecg.csv> --output <peaks.csv> --sampling_rate <hz>
Parameters:
--input — CSV file with single-column ECG signal data--output — Output CSV with peak sample indices--sampling_rate — Signal sampling rate in Hz (default: 150)scripts/extract_rsp.pyAnalyze respiration signals to extract breathing rate and peak times.
python scripts/extract_rsp.py --input <rsp.csv> --output <features.csv> --sampling_rate <hz>
[v1,v2,...] array notationneurokit2 package (pip install neurokit2)