NeuroKit: Difference between revisions

Latest revision as of 03:57, 13 February 2025

An open-source Python package for physiological signal processing.

NeuroKit2 is an open-source Python package designed for the processing and analysis of physiological signals. It is widely used in neuroscience, psychophysiology, and biomedical engineering for its comprehensive suite of tools that facilitate the extraction of meaningful information from raw physiological data.

Overview[edit]

NeuroKit2 provides a user-friendly interface for researchers and practitioners to analyze a variety of physiological signals, including ECG, EEG, EMG, and GSR. The package is designed to be accessible to users with varying levels of programming expertise, offering both high-level functions for quick analysis and low-level functions for more detailed investigations.

Features[edit]

NeuroKit2 includes a wide range of features that support the analysis of physiological data:

Signal Preprocessing: Functions for filtering, detrending, and normalizing signals to prepare them for analysis.
Feature Extraction: Tools for extracting features such as heart rate, heart rate variability, and respiratory rate from physiological signals.
Visualization: Capabilities for plotting signals and their features, aiding in the interpretation of data.
Event Detection: Algorithms for detecting events such as R-peaks in ECG signals or peaks in GSR data.
Statistical Analysis: Functions for performing statistical tests and analyses on physiological data.

Applications[edit]

NeuroKit2 is used in a variety of research and clinical settings. It is particularly valuable in studies of autonomic nervous system function, stress and emotion research, and sleep studies. Its ability to handle multiple types of physiological data makes it a versatile tool for interdisciplinary research.

Development and Community[edit]

NeuroKit2 is developed by a community of researchers and developers who contribute to its ongoing improvement and expansion. The project is hosted on GitHub, where users can access the source code, report issues, and contribute to the development of new features.

Related pages[edit]

@@ Line 1: / Line 1: @@
-'''NeuroKit''' is an open-source [[Python (programming language)|Python]] library designed for neurophysiological signal processing. It is primarily used in the field of [[psychology]] and [[neuroscience]] to process, analyze, and visualize physiological signals such as [[Electrocardiography|ECG]], [[Electroencephalography|EEG]], and [[Electromyography|EMG]].
+{{Short description|An open-source Python package for physiological signal processing.}}
-== Overview ==
+[[File:NeuroKit2_logo.png|thumb|right|NeuroKit2 logo]]
-[[NeuroKit]] was developed with the aim of providing a user-friendly and comprehensive tool for neurophysiological research. It simplifies the process of signal processing by providing a unified interface for different types of physiological signals. The library includes functions for signal processing, feature extraction, statistical analysis, and visualization.
+'''NeuroKit2''' is an open-source [[Python (programming language)|Python]] package designed for the processing and analysis of [[physiological signal|physiological signals]]. It is widely used in [[neuroscience]], [[psychophysiology]], and [[biomedical engineering]] for its comprehensive suite of tools that facilitate the extraction of meaningful information from raw physiological data.
-== Features ==
+==Overview==
+NeuroKit2 provides a user-friendly interface for researchers and practitioners to analyze a variety of physiological signals, including [[electrocardiogram|ECG]], [[electroencephalogram|EEG]], [[electromyogram|EMG]], and [[galvanic skin response|GSR]]. The package is designed to be accessible to users with varying levels of programming expertise, offering both high-level functions for quick analysis and low-level functions for more detailed investigations.
-[[NeuroKit]] offers a wide range of features for neurophysiological signal processing. These include:
+==Features==
+NeuroKit2 includes a wide range of features that support the analysis of physiological data:
-* '''Signal Processing''': NeuroKit provides tools for filtering, segmenting, and cleaning physiological signals. It supports a variety of signal types, including ECG, EEG, and EMG.
+* '''Signal Preprocessing''': Functions for filtering, detrending, and normalizing signals to prepare them for analysis.
+* '''Feature Extraction''': Tools for extracting features such as heart rate, heart rate variability, and respiratory rate from physiological signals.
+* '''Visualization''': Capabilities for plotting signals and their features, aiding in the interpretation of data.
+* '''Event Detection''': Algorithms for detecting events such as R-peaks in ECG signals or peaks in GSR data.
+* '''Statistical Analysis''': Functions for performing statistical tests and analyses on physiological data.
-* '''Feature Extraction''': The library includes functions for extracting features from physiological signals. These features can be used for further analysis or for building predictive models.
+==Applications==
+NeuroKit2 is used in a variety of research and clinical settings. It is particularly valuable in studies of [[autonomic nervous system|autonomic nervous system]] function, [[stress (biology)|stress]] and [[emotion]] research, and [[sleep study|sleep studies]]. Its ability to handle multiple types of physiological data makes it a versatile tool for interdisciplinary research.
-* '''Statistical Analysis''': NeuroKit includes functions for performing statistical analysis on the extracted features. This includes functions for hypothesis testing, correlation analysis, and regression analysis.
+==Development and Community==
+NeuroKit2 is developed by a community of researchers and developers who contribute to its ongoing improvement and expansion. The project is hosted on [[GitHub]], where users can access the source code, report issues, and contribute to the development of new features.
-* '''Visualization''': The library provides functions for visualizing physiological signals and the results of the analysis. This includes functions for plotting signals, features, and statistical results.
+==Related pages==
+* [[Physiological signal processing]]
+* [[Open-source software]]
+* [[Python (programming language)]]
+* [[Biomedical engineering]]
-== Usage ==
+[[Category:Open-source software]]
+[[Category:Python software]]
-[[NeuroKit]] is used in a wide range of applications in the field of psychology and neuroscience. These include:
+[[Category:Biomedical engineering]]
-* '''Psychophysiological Research''': NeuroKit is used in psychophysiological research to process and analyze physiological signals. This includes research in areas such as stress, emotion, and cognitive processes.
-* '''Neurofeedback''': The library is used in neurofeedback applications to process and analyze physiological signals in real-time. This allows for the development of biofeedback systems that can help individuals regulate their physiological responses.
-* '''Clinical Applications''': NeuroKit is also used in clinical settings for the processing and analysis of physiological signals. This includes applications in cardiology, neurology, and psychiatry.
-== See Also ==
-* [[Python (programming language)|Python]]
-* [[Electrocardiography|ECG]]
-* [[Electroencephalography|EEG]]
-* [[Electromyography|EMG]]
-[[Category:Python (programming language) libraries]]
-[[Category:Signal processing]]
-[[Category:Neuroscience]]
-[[Category:Psychology]]
-{{Python}}
-{{Neuroscience-stub}}
-{{Psychology-stub}}
-{{No image}}