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  • Introduction
    • Quick Example
    • Installation
    • Contributing
    • Documentation
      • General
      • Examples
    • Citation
    • Physiological Data Preprocessing
      • Simulate physiological signals
      • Electrodermal Activity (EDA/GSR)
      • Cardiac activity (ECG)
      • Respiration (RSP)
      • Electromyography (EMG)
      • Photoplethysmography (PPG/BVP)
      • Electrooculography (EOG)
      • Electrogastrography (EGG)
    • Physiological Data Analysis
      • Event-related
      • Interval-related
    • Miscellaneous
      • Heart Rate Variability (HRV)
      • ECG Delineation
      • Signal Processing
      • Complexity (Entropy, Fractal Dimensions, …)
      • Signal Decomposition
      • Signal Power Spectrum Density (PSD)
      • Statistics
    • Popularity
    • Notes
  • Authors
    • Core team
    • Contributors
  • Installation
    • 1. Python
      • Windows
        • Winpython
        • Miniconda or Anaconda
      • Mac OS
    • 2. NeuroKit
  • Get Started
    • Get familiar with Python in 10 minutes
      • Setup
      • Variables
      • Variables and data types
      • Lists and dictionnaries
      • Basic indexing
      • Indexing starts from 0
      • Control flow (if and else)
      • For loops
      • Functions
      • Packages
      • Lists vs. vectors (arrays)
      • Conditional indexing
      • Dataframes
      • Reading data
      • Next steps
    • Where to start
  • Examples
    • Try the examples in your browser
    • 1. Analysis Paradigm
      • a) Event-related paradigm
      • b) Interval-related paradigm
    • 2. Biosignal Processing
      • a) Custom processing pipeline
    • 3. Heart rate and heart cycles
      • a) Detecting components of the cardiac cycle
      • b) Looking closer at heart beats
    • 4. Electrodermal activity
      • a) Extracting information in EDA
    • 5. Respiration rate and respiration cycles
      • a) Extracting Respiration Rate Variability metrics
    • 6. Muscle activity
    • Simulate Artificial Physiological Signals
      • Cardiac Activity (ECG)
      • Respiration (RSP)
      • Electromyography (EMG)
      • Electrodermal Activity (EDA)
    • Customize your Processing Pipeline
      • The Default NeuroKit processing pipeline
      • Building your own process() function
      • Changing the processing parameters
      • Customize even more!
    • Event-related Analysis
      • The Dataset
      • Find Events
      • Process the Signals
      • Create Epochs
      • Extract Event Related Features
      • Plot Event Related Features
      • Important remarks:
    • Interval-related Analysis
      • The Dataset
      • Process the Signals
      • Extract Features
      • Optional: Segmenting the Data
    • Analyze Electrodermal Activity (EDA)
      • Extract the cleaned EDA signal
      • Locate Skin Conductance Response (SCR) features
      • Decompose EDA into Phasic and Tonic components
      • Quick Plot
    • Analyze Respiratory Rate Variability (RRV)
      • Download Data and Extract Relevant Signals
      • Analyse RRV
        • See documentation for full reference
    • ECG-Derived Respiration (EDR) Analysis
      • Download ECG Data
      • Extraction of ECG Features
      • Analyse EDR
    • Extract and Visualize Individual Heartbeats
      • Extract the cleaned ECG signal
      • Extract R-peaks location
      • Segment the signal around the heart beats
      • Advanced Plotting
        • Custom colors and legend
        • Interactivity
    • How to create epochs
      • One signal with multiple event markings
      • One subject with multiple data files
    • Complexity Analysis of Physiological Signals
      • Basic Concepts
        • Definitions
        • Time-delay embedding
        • Embedding Parameters
      • Entropy as measures of Complexity
        • Shannon Entropy (ShEn)
        • Approximate Entropy (ApEn)
        • Sample Entropy (SampEn)
        • Fuzzy Entropy (FuzzyEn)
        • Multiscale Entropy (MSE)
      • Detrended Fluctuation Analysis (DFA)
    • Analyze Electrooculography EOG data (eye blinks, saccades, etc.)
      • Explore the EOG signal
      • Clean the signal
      • Detect and visualize eye blinks
    • Fit a function to a signal
      • Fit a linear function
      • Non-linear curves
  • Resources
    • Recording good quality signals
      • Recording
      • Signal quality
      • Artifacts and Anomalies
    • What software for physiological signal processing
      • Software vs. programming language (packages)
      • GUI vs. code
      • Matlab vs. Python vs. R vs. Julia
    • Additional Resources
      • General Neuroimaging
      • ECG
      • EDA
      • EEG
  • Functions
    • ECG
    • PPG
    • HRV
    • RSP
    • EDA
    • EMG
    • EEG
    • Signal Processing
    • Events
    • Data
    • Epochs
    • Statistics
    • Complexity
    • Miscellaneous
  • Benchmarks
    • Benchmarking of ECG Preprocessing Methods
      • Introduction
      • Databases
        • Glasgow University Database
        • MIT-BIH Arrhythmia Database
        • MIT-BIH Normal Sinus Rhythm Database
        • Concanate them together
      • Study 1: Comparing Different R-Peaks Detection Algorithms
        • Procedure
        • Results
        • Conclusion
      • Study 2: Normalization
        • Procedure
        • Results
        • Conclusion
    • References
  • Datasets
    • ECG (1000 hz)
    • ECG - pandas (3000 hz)
    • Event-related (4 events)
    • Resting state (5 min)
    • Resting state (8 min)
  • Contributing
    • Understanding NeuroKit
      • 1. readthedocs
        • Example
      • 2. The code on Github
        • Example
      • 3. The code on YOUR machine
        • Python directory
        • Windows
        • Mac
        • Example
    • Contributing guide
      • NeuroKit’s style
        • Structure and code
        • Run code checks
        • Avoid Semantic Errors
        • Development workflow
      • How to use GitHub to contribute
        • Step 1: Fork it
        • Step 2: Clone it
        • Step 3: Find it and fix it
        • Step 4: Commit it and push it
        • Step 4: Create pull request
        • Step 5: Let’s do it
      • Useful reads
      • What’s next?
    • Ideas for first contributions
      • Look for “good first contribution” issues
      • Improving documentation
      • Adding tests
      • Adding examples and tutorials
        • How to write
        • Where to add the files
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© Copyright 2020, Dominique Makowski Revision a90f157b.