Does it integrate with other data science tools?

Yes, NeuroKit2 is built on the Python scientific stack and integrates well with libraries like NumPy, Pandas, and MNE-Python for advanced EEG workflows.

What biosignals can I process with NeuroKit2?

NeuroKit2 supports a wide range of signals including ECG (heart), EEG (brain), EDA/GSR (skin conductance), RSP (respiration), EMG (muscle), EOG (eye), and PPG (pulse).

Does this skill support Heart Rate Variability (HRV)?

Yes, it provides comprehensive HRV analysis across time, frequency, and nonlinear domains, including specialized metrics like Respiratory Sinus Arrhythmia (RSA).

Can I process multiple signals simultaneously?

Absolutely. The skill includes multi-modal integration capabilities to process and analyze different physiological signals (e.g., ECG and EDA) at the same time.

What type of analysis modes are available?

It automatically switches between Event-Related analysis (for short, stimulus-locked responses) and Interval-Related analysis (for longer periods like resting state or continuous monitoring).

NeuroKit2 Biosignal Processing

Name: NeuroKit2 Biosignal Processing
Author: Activer007

byActiver007

Ciencia de Datos y ML

Processes and analyzes physiological biosignals including ECG, EEG, EDA, and respiratory patterns for psychophysiology and clinical research.

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NeuroKit2 for Claude Code provides a high-level interface for the comprehensive Python biosignal processing toolkit, enabling seamless analysis of physiological data. It automates complex workflows for cardiovascular, neural, and autonomic signals, offering advanced features like heart rate variability (HRV) computation, EEG microstate segmentation, and skin conductance response detection. Designed for researchers and developers in fields ranging from human-computer interaction to clinical psychophysiology, this skill simplifies the extraction of meaningful scientific indices from raw, multi-modal signal data.

Características Principales

Advanced EEG analysis for frequency bands, microstates, and source localization
Multi-modal signal integration for synchronized processing of various physiological streams
Autonomic nervous system assessment via EDA/GSR decomposition and SCR detection
Comprehensive cardiac analysis including ECG/PPG processing and detailed HRV metrics
0 GitHub stars
Advanced complexity analysis using entropy measures, fractal dimensions, and nonlinear dynamics

Casos de Uso

Analyzing heart rate variability and respiratory patterns for stress and autonomic research
Decomposing skin conductance signals to measure emotional arousal and sympathetic activity
Processing EEG data for cognitive load assessment, microstate analysis, or sleep studies

Acerca de

Características Principales

Advanced EEG analysis for frequency bands, microstates, and source localization
Multi-modal signal integration for synchronized processing of various physiological streams
Autonomic nervous system assessment via EDA/GSR decomposition and SCR detection
Comprehensive cardiac analysis including ECG/PPG processing and detailed HRV metrics
0 GitHub stars
Advanced complexity analysis using entropy measures, fractal dimensions, and nonlinear dynamics

Casos de Uso

Analyzing heart rate variability and respiratory patterns for stress and autonomic research
Decomposing skin conductance signals to measure emotional arousal and sympathetic activity
Processing EEG data for cognitive load assessment, microstate analysis, or sleep studies