{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"NeuroKit2 processes physiological signals (ECG, RSP, EDA, EMG, PPG, EOG) with high-level functions that handle filtering, peak detection, and feature extraction in minimal code.","skillfed_tags":["biosignal-processing","physiological-data","signal-analysis"],"use_cases":["Preprocess and extract heart rate variability features from ECG recordings for cardiovascular research.","Analyze electrodermal activity (skin conductance) peaks and troughs in emotion or stress studies.","Extract respiratory rate and variability metrics from respiration signals in sleep or exercise studies.","Simulate synthetic physiological signals to validate custom analysis pipelines before applying them to real data.","Perform event-related analysis by aligning and processing multiple signal types around experimental events.","Locate and delineate ECG waveform components (P, Q, R, S, T waves) for detailed cardiac morphology analysis."],"what_it_does":"NeuroKit2 is a Python toolbox for processing and analyzing physiological signals commonly recorded in research and clinical settings. It wraps complex signal processing operations (filtering, peak detection, feature extraction) into high-level functions that researchers and clinicians can call with minimal code\u2014the description example shows analysis completed in two function calls. The package supports multiple signal types: ECG (electrocardiogram), RSP (respiration), EDA (electrodermal activity), EMG (electromyography), PPG (photoplethysmography), and EOG (electrooculography).\n\nThe package is built on established scientific libraries (numpy, scipy, scikit-learn, pandas, matplotlib) and provides both simple \"master\" functions for standard workflows and lower-level functions for custom pipelines. It includes example datasets and supports signal simulation for testing. The codebase is actively maintained, has a published peer-reviewed paper (2021), and is designed to be accessible to users without deep expertise in signal processing or programming.","worth_installing":"Yes, if you work with physiological signals and want to avoid building signal processing pipelines from scratch. The package is actively maintained, permissively licensed, has low install friction, and is backed by published research. The Pre-Alpha classifier is a minor concern\u2014verify current stability for production use\u2014but the active repository, recent releases, and substantial community adoption suggest it is reliable for research and clinical applications."},"id":"neurokit2","links":{"html":"https://skillfed.io/packages/neurokit2","md":"https://skillfed.io/packages/neurokit2.md","pypi":"https://pypi.org/project/neurokit2/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-02","license_spdx":null,"license_treatment":"permissive","name":"neurokit2","python_support":"supports_current","summary":"The Python Toolbox for Neurophysiological Signal Processing."},"popularity":{"monthly_downloads":175694,"position":10260,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.13"}