[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFAAHV9P6C935WT0Q6FV9F":3,"skill-same:01M6XFAAHV9P6C935WT0Q6FV9F":116},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":14,"repositoryHtmlUrl":49,"repositoryHomepageUrl":6,"ownerHtmlUrl":50,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":51,"install":52,"resources":65},"01M6XFAAHV9P6C935WT0Q6FV9F","neurokit2",null,"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.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"signals","when","signal","for","event","multi","processing","biology","synthetic","brain","research","biosignal","complexity","use","system","analysis","integration","toolkit","assessment","2026-11-10 10:12:30",false,"---\nname: neurokit2\ndescription: 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.\ncategory: biology\nlicense: MIT license\nmetadata:\n    skill-author: Synthetic Sciences\n---\n\n# NeuroKit2\n\n## Overview\n\nNeuroKit2 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.\n\n## When to Use This Skill\n\nApply this skill when working with:\n- **Cardiac signals**: ECG, PPG, heart rate variability (HRV), pulse analysis\n- **Brain signals**: EEG frequency bands, microstates, complexity, source localization\n- **Autonomic signals**: Electrodermal activity (EDA\u002FGSR), skin conductance responses (SCR)\n- **Respiratory signals**: Breathing rate, respiratory variability (RRV), volume per time\n- **Muscular signals**: EMG amplitude, muscle activation detection\n- **Eye tracking**: EOG, blink detection and analysis\n- **Multi-modal integration**: Processing multiple physiological signals simultaneously\n- **Complexity analysis**: Entropy measures, fractal dimensions, nonlinear dynamics\n\n## Core Capabilities\n\n### 1. Cardiac Signal Processing (ECG\u002FPPG)\n\nProcess electrocardiogram and photoplethysmography signals for cardiovascular analysis. See `references\u002Fecg_cardiac.md` for detailed workflows.\n\n**Primary workflows:**\n- ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment\n- HRV analysis across time, frequency, and nonlinear domains\n- PPG pulse analysis and quality assessment\n- ECG-derived respiration extraction\n\n**Key functions:**\n```python\nimport neurokit2 as nk\n\n# Complete ECG processing pipeline\nsignals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)\n\n# Analyze ECG data (event-related or interval-related)\nanalysis = nk.ecg_analyze(signals, sampling_rate=1000)\n\n# Comprehensive HRV analysis\nhrv = nk.hrv(peaks, sampling_rate=1000)  # Time, frequency, nonlinear domains\n```\n\n### 2. Heart Rate Variability Analysis\n\nCompute comprehensive HRV metrics from cardiac signals. See `references\u002Fhrv.md` for all indices and domain-specific analysis.\n\n**Supported domains:**\n- **Time domain**: SDNN, RMSSD, pNN50, SDSD, and derived metrics\n- **Frequency domain**: ULF, VLF, LF, HF, VHF power and ratios\n- **Nonlinear domain**: Poincaré plot (SD1\u002FSD2), entropy measures, fractal dimensions\n- **Specialized**: Respiratory sinus arrhythmia (RSA), recurrence quantification analysis (RQA)\n\n**Key functions:**\n```python\n# All HRV indices at once\nhrv_indices = nk.hrv(peaks, sampling_rate=1000)\n\n# Domain-specific analysis\nhrv_time = nk.hrv_time(peaks)\nhrv_freq = nk.hrv_frequency(peaks, sampling_rate=1000)\nhrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=1000)\nhrv_rsa = nk.hrv_rsa(peaks, rsp_signal, sampling_rate=1000)\n```\n\n### 3. Brain Signal Analysis (EEG)\n\nAnalyze electroencephalography signals for frequency power, complexity, and microstate patterns. See `references\u002Feeg.md` for detailed workflows and MNE integration.\n\n**Primary capabilities:**\n- Frequency band power analysis (Delta, Theta, Alpha, Beta, Gamma)\n- Channel quality assessment and re-referencing\n- Source localization (sLORETA, MNE)\n- Microstate segmentation and transition dynamics\n- Global field power and dissimilarity measures\n\n**Key functions:**\n```python\n# Power analysis across frequency bands\npower = nk.eeg_power(eeg_data, sampling_rate=250, channels=['Fz', 'Cz', 'Pz'])\n\n# Microstate analysis\nmicrostates = nk.microstates_segment(eeg_data, n_microstates=4, method='kmod')\nstatic = nk.microstates_static(microstates)\ndynamic = nk.microstates_dynamic(microstates)\n```\n\n### 4. Electrodermal Activity (EDA)\n\nProcess skin conductance signals for autonomic nervous system assessment. See `references\u002Feda.md` for detailed workflows.\n\n**Primary workflows:**\n- Signal decomposition into tonic and phasic components\n- Skin conductance response (SCR) detection and analysis\n- Sympathetic nervous system index calculation\n- Autocorrelation and changepoint detection\n\n**Key functions:**\n```python\n# Complete EDA processing\nsignals, info = nk.eda_process(eda_signal, sampling_rate=100)\n\n# Analyze EDA data\nanalysis = nk.eda_analyze(signals, sampling_rate=100)\n\n# Sympathetic nervous system activity\nsympathetic = nk.eda_sympathetic(signals, sampling_rate=100)\n```\n\n### 5. Respiratory Signal Processing (RSP)\n\nAnalyze breathing patterns and respiratory variability. See `references\u002Frsp.md` for detailed workflows.\n\n**Primary capabilities:**\n- Respiratory rate calculation and variability analysis\n- Breathing amplitude and symmetry assessment\n- Respiratory volume per time (fMRI applications)\n- Respiratory amplitude variability (RAV)\n\n**Key functions:**\n```python\n# Complete RSP processing\nsignals, info = nk.rsp_process(rsp_signal, sampling_rate=100)\n\n# Respiratory rate variability\nrrv = nk.rsp_rrv(signals, sampling_rate=100)\n\n# Respiratory volume per time\nrvt = nk.rsp_rvt(signals, sampling_rate=100)\n```\n\n### 6. Electromyography (EMG)\n\nProcess muscle activity signals for activation detection and amplitude analysis. See `references\u002Femg.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EMG processing\nsignals, info = nk.emg_process(emg_signal, sampling_rate=1000)\n\n# Muscle activation detection\nactivation = nk.emg_activation(signals, sampling_rate=1000, method='threshold')\n```\n\n### 7. Electrooculography (EOG)\n\nAnalyze eye movement and blink patterns. See `references\u002Feog.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EOG processing\nsignals, info = nk.eog_process(eog_signal, sampling_rate=500)\n\n# Extract blink features\nfeatures = nk.eog_features(signals, sampling_rate=500)\n```\n\n### 8. General Signal Processing\n\nApply filtering, decomposition, and transformation operations to any signal. See `references\u002Fsignal_processing.md` for comprehensive utilities.\n\n**Key operations:**\n- Filtering (lowpass, highpass, bandpass, bandstop)\n- Decomposition (EMD, SSA, wavelet)\n- Peak detection and correction\n- Power spectral density estimation\n- Signal interpolation and resampling\n- Autocorrelation and synchrony analysis\n\n**Key functions:**\n```python\n# Filtering\nfiltered = nk.signal_filter(signal, sampling_rate=1000, lowcut=0.5, highcut=40)\n\n# Peak detection\npeaks = nk.signal_findpeaks(signal)\n\n# Power spectral density\npsd = nk.signal_psd(signal, sampling_rate=1000)\n```\n\n### 9. Complexity and Entropy Analysis\n\nCompute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See `references\u002Fcomplexity.md` for all available metrics.\n\n**Available measures:**\n- **Entropy**: Shannon, approximate, sample, permutation, spectral, fuzzy, multiscale\n- **Fractal dimensions**: Katz, Higuchi, Petrosian, Sevcik, correlation dimension\n- **Nonlinear dynamics**: Lyapunov exponents, Lempel-Ziv complexity, recurrence quantification\n- **DFA**: Detrended fluctuation analysis, multifractal DFA\n- **Information theory**: Fisher information, mutual information\n\n**Key functions:**\n```python\n# Multiple complexity metrics at once\ncomplexity_indices = nk.complexity(signal, sampling_rate=1000)\n\n# Specific measures\napen = nk.entropy_approximate(signal)\ndfa = nk.fractal_dfa(signal)\nlyap = nk.complexity_lyapunov(signal, sampling_rate=1000)\n```\n\n### 10. Event-Related Analysis\n\nCreate epochs around stimulus events and analyze physiological responses. See `references\u002Fepochs_events.md` for workflows.\n\n**Primary capabilities:**\n- Epoch creation from event markers\n- Event-related averaging and visualization\n- Baseline correction options\n- Grand average computation with confidence intervals\n\n**Key functions:**\n```python\n# Find events in signal\nevents = nk.events_find(trigger_signal, threshold=0.5)\n\n# Create epochs around events\nepochs = nk.epochs_create(signals, events, sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Average across epochs\ngrand_average = nk.epochs_average(epochs)\n```\n\n### 11. Multi-Signal Integration\n\nProcess multiple physiological signals simultaneously with unified output. See `references\u002Fbio_module.md` for integration workflows.\n\n**Key functions:**\n```python\n# Process multiple signals at once\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    emg=emg_signal,\n    sampling_rate=1000\n)\n\n# Analyze all processed signals\nbio_analysis = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n## Analysis Modes\n\nNeuroKit2 automatically selects between two analysis modes based on data duration:\n\n**Event-related analysis** (\u003C 10 seconds):\n- Analyzes stimulus-locked responses\n- Epoch-based segmentation\n- Suitable for experimental paradigms with discrete trials\n\n**Interval-related analysis** (≥ 10 seconds):\n- Characterizes physiological patterns over extended periods\n- Resting state or continuous activities\n- Suitable for baseline measurements and long-term monitoring\n\nMost `*_analyze()` functions automatically choose the appropriate mode.\n\n## Installation\n\n```bash\nuv pip install neurokit2\n```\n\nFor development version:\n```bash\nuv pip install https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\u002Fzipball\u002Fdev\n```\n\n## Common Workflows\n\n### Quick Start: ECG Analysis\n```python\nimport neurokit2 as nk\n\n# Load example data\necg = nk.ecg_simulate(duration=60, sampling_rate=1000)\n\n# Process ECG\nsignals, info = nk.ecg_process(ecg, sampling_rate=1000)\n\n# Analyze HRV\nhrv = nk.hrv(info['ECG_R_Peaks'], sampling_rate=1000)\n\n# Visualize\nnk.ecg_plot(signals, info)\n```\n\n### Multi-Modal Analysis\n```python\n# Process multiple signals\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    sampling_rate=1000\n)\n\n# Analyze all signals\nresults = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n### Event-Related Potential\n```python\n# Find events\nevents = nk.events_find(trigger_channel, threshold=0.5)\n\n# Create epochs\nepochs = nk.epochs_create(processed_signals, events,\n                          sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Event-related analysis for each signal type\necg_epochs = nk.ecg_eventrelated(epochs)\neda_epochs = nk.eda_eventrelated(epochs)\n```\n\n## References\n\nThis skill includes comprehensive reference documentation organized by signal type and analysis method:\n\n- **ecg_cardiac.md**: ECG\u002FPPG processing, R-peak detection, delineation, quality assessment\n- **hrv.md**: Heart rate variability indices across all domains\n- **eeg.md**: EEG analysis, frequency bands, microstates, source localization\n- **eda.md**: Electrodermal activity processing and SCR analysis\n- **rsp.md**: Respiratory signal processing and variability\n- **ppg.md**: Photoplethysmography signal analysis\n- **emg.md**: Electromyography processing and activation detection\n- **eog.md**: Electrooculography and blink analysis\n- **signal_processing.md**: General signal utilities and transformations\n- **complexity.md**: Entropy, fractal, and nonlinear measures\n- **epochs_events.md**: Event-related analysis and epoch creation\n- **bio_module.md**: Multi-signal integration workflows\n\nLoad specific reference files as needed using the Read tool to access detailed function documentation and parameters.\n\n## Additional Resources\n\n- Official Documentation: https:\u002F\u002Fneuropsychology.github.io\u002FNeuroKit\u002F\n- GitHub Repository: https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\n- Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13428-020-01516-y\n\n","# NeuroKit2\n\n## Overview\n\nNeuroKit2 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.\n\n## When to Use This Skill\n\nApply this skill when working with:\n- **Cardiac signals**: ECG, PPG, heart rate variability (HRV), pulse analysis\n- **Brain signals**: EEG frequency bands, microstates, complexity, source localization\n- **Autonomic signals**: Electrodermal activity (EDA\u002FGSR), skin conductance responses (SCR)\n- **Respiratory signals**: Breathing rate, respiratory variability (RRV), volume per time\n- **Muscular signals**: EMG amplitude, muscle activation detection\n- **Eye tracking**: EOG, blink detection and analysis\n- **Multi-modal integration**: Processing multiple physiological signals simultaneously\n- **Complexity analysis**: Entropy measures, fractal dimensions, nonlinear dynamics\n\n## Core Capabilities\n\n### 1. Cardiac Signal Processing (ECG\u002FPPG)\n\nProcess electrocardiogram and photoplethysmography signals for cardiovascular analysis. See `references\u002Fecg_cardiac.md` for detailed workflows.\n\n**Primary workflows:**\n- ECG processing pipeline: cleaning → R-peak detection → delineation → quality assessment\n- HRV analysis across time, frequency, and nonlinear domains\n- PPG pulse analysis and quality assessment\n- ECG-derived respiration extraction\n\n**Key functions:**\n```python\nimport neurokit2 as nk\n\n# Complete ECG processing pipeline\nsignals, info = nk.ecg_process(ecg_signal, sampling_rate=1000)\n\n# Analyze ECG data (event-related or interval-related)\nanalysis = nk.ecg_analyze(signals, sampling_rate=1000)\n\n# Comprehensive HRV analysis\nhrv = nk.hrv(peaks, sampling_rate=1000)  # Time, frequency, nonlinear domains\n```\n\n### 2. Heart Rate Variability Analysis\n\nCompute comprehensive HRV metrics from cardiac signals. See `references\u002Fhrv.md` for all indices and domain-specific analysis.\n\n**Supported domains:**\n- **Time domain**: SDNN, RMSSD, pNN50, SDSD, and derived metrics\n- **Frequency domain**: ULF, VLF, LF, HF, VHF power and ratios\n- **Nonlinear domain**: Poincaré plot (SD1\u002FSD2), entropy measures, fractal dimensions\n- **Specialized**: Respiratory sinus arrhythmia (RSA), recurrence quantification analysis (RQA)\n\n**Key functions:**\n```python\n# All HRV indices at once\nhrv_indices = nk.hrv(peaks, sampling_rate=1000)\n\n# Domain-specific analysis\nhrv_time = nk.hrv_time(peaks)\nhrv_freq = nk.hrv_frequency(peaks, sampling_rate=1000)\nhrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=1000)\nhrv_rsa = nk.hrv_rsa(peaks, rsp_signal, sampling_rate=1000)\n```\n\n### 3. Brain Signal Analysis (EEG)\n\nAnalyze electroencephalography signals for frequency power, complexity, and microstate patterns. See `references\u002Feeg.md` for detailed workflows and MNE integration.\n\n**Primary capabilities:**\n- Frequency band power analysis (Delta, Theta, Alpha, Beta, Gamma)\n- Channel quality assessment and re-referencing\n- Source localization (sLORETA, MNE)\n- Microstate segmentation and transition dynamics\n- Global field power and dissimilarity measures\n\n**Key functions:**\n```python\n# Power analysis across frequency bands\npower = nk.eeg_power(eeg_data, sampling_rate=250, channels=['Fz', 'Cz', 'Pz'])\n\n# Microstate analysis\nmicrostates = nk.microstates_segment(eeg_data, n_microstates=4, method='kmod')\nstatic = nk.microstates_static(microstates)\ndynamic = nk.microstates_dynamic(microstates)\n```\n\n### 4. Electrodermal Activity (EDA)\n\nProcess skin conductance signals for autonomic nervous system assessment. See `references\u002Feda.md` for detailed workflows.\n\n**Primary workflows:**\n- Signal decomposition into tonic and phasic components\n- Skin conductance response (SCR) detection and analysis\n- Sympathetic nervous system index calculation\n- Autocorrelation and changepoint detection\n\n**Key functions:**\n```python\n# Complete EDA processing\nsignals, info = nk.eda_process(eda_signal, sampling_rate=100)\n\n# Analyze EDA data\nanalysis = nk.eda_analyze(signals, sampling_rate=100)\n\n# Sympathetic nervous system activity\nsympathetic = nk.eda_sympathetic(signals, sampling_rate=100)\n```\n\n### 5. Respiratory Signal Processing (RSP)\n\nAnalyze breathing patterns and respiratory variability. See `references\u002Frsp.md` for detailed workflows.\n\n**Primary capabilities:**\n- Respiratory rate calculation and variability analysis\n- Breathing amplitude and symmetry assessment\n- Respiratory volume per time (fMRI applications)\n- Respiratory amplitude variability (RAV)\n\n**Key functions:**\n```python\n# Complete RSP processing\nsignals, info = nk.rsp_process(rsp_signal, sampling_rate=100)\n\n# Respiratory rate variability\nrrv = nk.rsp_rrv(signals, sampling_rate=100)\n\n# Respiratory volume per time\nrvt = nk.rsp_rvt(signals, sampling_rate=100)\n```\n\n### 6. Electromyography (EMG)\n\nProcess muscle activity signals for activation detection and amplitude analysis. See `references\u002Femg.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EMG processing\nsignals, info = nk.emg_process(emg_signal, sampling_rate=1000)\n\n# Muscle activation detection\nactivation = nk.emg_activation(signals, sampling_rate=1000, method='threshold')\n```\n\n### 7. Electrooculography (EOG)\n\nAnalyze eye movement and blink patterns. See `references\u002Feog.md` for workflows.\n\n**Key functions:**\n```python\n# Complete EOG processing\nsignals, info = nk.eog_process(eog_signal, sampling_rate=500)\n\n# Extract blink features\nfeatures = nk.eog_features(signals, sampling_rate=500)\n```\n\n### 8. General Signal Processing\n\nApply filtering, decomposition, and transformation operations to any signal. See `references\u002Fsignal_processing.md` for comprehensive utilities.\n\n**Key operations:**\n- Filtering (lowpass, highpass, bandpass, bandstop)\n- Decomposition (EMD, SSA, wavelet)\n- Peak detection and correction\n- Power spectral density estimation\n- Signal interpolation and resampling\n- Autocorrelation and synchrony analysis\n\n**Key functions:**\n```python\n# Filtering\nfiltered = nk.signal_filter(signal, sampling_rate=1000, lowcut=0.5, highcut=40)\n\n# Peak detection\npeaks = nk.signal_findpeaks(signal)\n\n# Power spectral density\npsd = nk.signal_psd(signal, sampling_rate=1000)\n```\n\n### 9. Complexity and Entropy Analysis\n\nCompute nonlinear dynamics, fractal dimensions, and information-theoretic measures. See `references\u002Fcomplexity.md` for all available metrics.\n\n**Available measures:**\n- **Entropy**: Shannon, approximate, sample, permutation, spectral, fuzzy, multiscale\n- **Fractal dimensions**: Katz, Higuchi, Petrosian, Sevcik, correlation dimension\n- **Nonlinear dynamics**: Lyapunov exponents, Lempel-Ziv complexity, recurrence quantification\n- **DFA**: Detrended fluctuation analysis, multifractal DFA\n- **Information theory**: Fisher information, mutual information\n\n**Key functions:**\n```python\n# Multiple complexity metrics at once\ncomplexity_indices = nk.complexity(signal, sampling_rate=1000)\n\n# Specific measures\napen = nk.entropy_approximate(signal)\ndfa = nk.fractal_dfa(signal)\nlyap = nk.complexity_lyapunov(signal, sampling_rate=1000)\n```\n\n### 10. Event-Related Analysis\n\nCreate epochs around stimulus events and analyze physiological responses. See `references\u002Fepochs_events.md` for workflows.\n\n**Primary capabilities:**\n- Epoch creation from event markers\n- Event-related averaging and visualization\n- Baseline correction options\n- Grand average computation with confidence intervals\n\n**Key functions:**\n```python\n# Find events in signal\nevents = nk.events_find(trigger_signal, threshold=0.5)\n\n# Create epochs around events\nepochs = nk.epochs_create(signals, events, sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Average across epochs\ngrand_average = nk.epochs_average(epochs)\n```\n\n### 11. Multi-Signal Integration\n\nProcess multiple physiological signals simultaneously with unified output. See `references\u002Fbio_module.md` for integration workflows.\n\n**Key functions:**\n```python\n# Process multiple signals at once\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    emg=emg_signal,\n    sampling_rate=1000\n)\n\n# Analyze all processed signals\nbio_analysis = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n## Analysis Modes\n\nNeuroKit2 automatically selects between two analysis modes based on data duration:\n\n**Event-related analysis** (\u003C 10 seconds):\n- Analyzes stimulus-locked responses\n- Epoch-based segmentation\n- Suitable for experimental paradigms with discrete trials\n\n**Interval-related analysis** (≥ 10 seconds):\n- Characterizes physiological patterns over extended periods\n- Resting state or continuous activities\n- Suitable for baseline measurements and long-term monitoring\n\nMost `*_analyze()` functions automatically choose the appropriate mode.\n\n## Installation\n\n```bash\nuv pip install neurokit2\n```\n\nFor development version:\n```bash\nuv pip install https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\u002Fzipball\u002Fdev\n```\n\n## Common Workflows\n\n### Quick Start: ECG Analysis\n```python\nimport neurokit2 as nk\n\n# Load example data\necg = nk.ecg_simulate(duration=60, sampling_rate=1000)\n\n# Process ECG\nsignals, info = nk.ecg_process(ecg, sampling_rate=1000)\n\n# Analyze HRV\nhrv = nk.hrv(info['ECG_R_Peaks'], sampling_rate=1000)\n\n# Visualize\nnk.ecg_plot(signals, info)\n```\n\n### Multi-Modal Analysis\n```python\n# Process multiple signals\nbio_signals, bio_info = nk.bio_process(\n    ecg=ecg_signal,\n    rsp=rsp_signal,\n    eda=eda_signal,\n    sampling_rate=1000\n)\n\n# Analyze all signals\nresults = nk.bio_analyze(bio_signals, sampling_rate=1000)\n```\n\n### Event-Related Potential\n```python\n# Find events\nevents = nk.events_find(trigger_channel, threshold=0.5)\n\n# Create epochs\nepochs = nk.epochs_create(processed_signals, events,\n                          sampling_rate=1000,\n                          epochs_start=-0.5, epochs_end=2.0)\n\n# Event-related analysis for each signal type\necg_epochs = nk.ecg_eventrelated(epochs)\neda_epochs = nk.eda_eventrelated(epochs)\n```\n\n## References\n\nThis skill includes comprehensive reference documentation organized by signal type and analysis method:\n\n- **ecg_cardiac.md**: ECG\u002FPPG processing, R-peak detection, delineation, quality assessment\n- **hrv.md**: Heart rate variability indices across all domains\n- **eeg.md**: EEG analysis, frequency bands, microstates, source localization\n- **eda.md**: Electrodermal activity processing and SCR analysis\n- **rsp.md**: Respiratory signal processing and variability\n- **ppg.md**: Photoplethysmography signal analysis\n- **emg.md**: Electromyography processing and activation detection\n- **eog.md**: Electrooculography and blink analysis\n- **signal_processing.md**: General signal utilities and transformations\n- **complexity.md**: Entropy, fractal, and nonlinear measures\n- **epochs_events.md**: Event-related analysis and epoch creation\n- **bio_module.md**: Multi-signal integration workflows\n\nLoad specific reference files as needed using the Read tool to access detailed function documentation and parameters.\n\n## Additional Resources\n\n- Official Documentation: https:\u002F\u002Fneuropsychology.github.io\u002FNeuroKit\u002F\n- GitHub Repository: https:\u002F\u002Fgithub.com\u002Fneuropsychology\u002FNeuroKit\n- Publication: Makowski et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https:\u002F\u002Fdoi.org\u002F10.3758\u002Fs13428-020-01516-y\n\n",{"name":5,"license":43,"category":26,"metadata":44,"description":7},"MIT license",{"skill-author":45},"Synthetic Sciences","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add fadrienne\u002FMyGitHub --skill neurokit2",{"tool":58,"command":59},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill neurokit2",{"tool":61,"command":62},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill neurokit2","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002FSKILL.md",true,[66,72,76,80,84,88,92,96,100,104,108,112],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"references\u002Fbio_module.md","REFERENCE","参考文档",13099,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Fbio_module.md",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fcomplexity.md",17352,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Fcomplexity.md",{"path":77,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fecg_cardiac.md",10810,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Fecg_cardiac.md",{"path":81,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Feda.md",14733,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Feda.md",{"path":85,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Feeg.md",14306,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Feeg.md",{"path":89,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Femg.md",12390,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Femg.md",{"path":93,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Feog.md",12271,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Feog.md",{"path":97,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Fepochs_events.md",14793,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Fepochs_events.md",{"path":101,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fhrv.md",15493,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Fhrv.md",{"path":105,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002Fppg.md",11168,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Fppg.md",{"path":109,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":110,"sourceHtmlUrl":111},"references\u002Frsp.md",13946,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Frsp.md",{"path":113,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":114,"sourceHtmlUrl":115},"references\u002Fsignal_processing.md",16652,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fbiology\u002Fneurokit2\u002Freferences\u002Fsignal_processing.md",[117,147,171,205,232,259],{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":128,"publishedAt":38},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[129,130,131,132,133,134,135,136,20,137,138,34,139,140,141,142,143,144,145,146],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":156,"publishedAt":38},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[153],{"categoryNo":154,"categoryName":155,"taxonomyType":126,"taxonomyTypeDesc":127},"01JBSEEDCAT000000000000008","测试与质量",[34,157,129,158,159,160,20,161,162,163,22,164,32,165,166,29,167,168,169,170],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","with","test","results","statsmodels","models","help",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":186,"publishedAt":38},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[177,180,181],{"categoryNo":178,"categoryName":179,"taxonomyType":126,"taxonomyTypeDesc":127},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},{"categoryNo":182,"categoryName":183,"taxonomyType":184,"taxonomyTypeDesc":185},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[187,188,189,190,191,192,193,194,195,196,137,197,198,199,200,201,202,203,162,204],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":215,"publishedAt":38},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[211,214],{"categoryNo":212,"categoryName":213,"taxonomyType":126,"taxonomyTypeDesc":127},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},[216,188,217,218,219,220,221,222,223,195,20,224,225,226,227,228,229,230,22,231],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":244,"publishedAt":38},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[238,241],{"categoryNo":239,"categoryName":240,"taxonomyType":126,"taxonomyTypeDesc":127},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":242,"categoryName":243,"taxonomyType":184,"taxonomyTypeDesc":185},"01JBSEEDCAT000000000000030","开发工程师",[245,246,29,247,248,249,250,136,251,20,137,34,252,253,254,255,256,257,203,258],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":260,"name":261,"nameZh":6,"description":262,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":263,"licenseFileUrl":6,"sourceHtmlUrl":264,"categories":265,"keywords":272,"publishedAt":38},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[266,269,270,271],{"categoryNo":267,"categoryName":268,"taxonomyType":126,"taxonomyTypeDesc":127},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":178,"categoryName":179,"taxonomyType":126,"taxonomyTypeDesc":127},{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},{"categoryNo":182,"categoryName":183,"taxonomyType":184,"taxonomyTypeDesc":185},[187,273,274,275,276,277,216,278,279,20,137,34,280,281,282,283,284,285,188,203],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]