Agent skill · research science · aiskillstore
neurokit2
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
Why this skill is useful
Provides specialized functions for analyzing various physiological signals that the AI wouldn't reliably generate on its own.
What it needs
Requires neurokit2 installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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/ecgcardiac.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: 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: 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/neurokit2--cfd5ae