Clean and analyze physiological biosignals from raw time-series data.
npx skills add https://github.com/shushuzn/Rairos --skill neurokit2-shushuzn
Please help me install this Agent Skill. Skill: neurokit2 Source: https://github.com/shushuzn/Rairos/tree/main/skills/neurokit2 Command: npx skills add https://github.com/shushuzn/Rairos --skill neurokit2-shushuzn
NeuroKit2 streamlines the processing of physiological time-series (biosignals) into cleaned signals and actionable metrics, so researchers can analyze cardiovascular, neural, autonomic, respiratory, muscular, and eye-movement data without hand-built pipelines.
Use the neurokit2 skill to process the provided physiological recordings and compute cleaned signals plus HRV/EDA/respiratory/EEG metrics as appropriate.
High-intent search queries and answers about installing and using this skill.
Computing HRV metrics from raw ECG signals involves cleaning the data, detecting R-peaks, and calculating time, frequency, and nonlinear domain features. This skill processes cardiovascular recordings end-to-end to produce actionable HRV summaries without hand-built pipelines.
Analyzing EDA and ECG data together for event-related research requires creating stimulus-locked epochs and computing cross-signal features. This skill supports multi-modal integration to calculate metrics like cardio-EDA coupling from simultaneous physiological recordings.
Yes, you can extract EEG frequency power and microstates from raw time-series data. The skill applies filtering and decomposition to neural signals, yielding complexity and entropy measures suitable for psychophysiology and neuroscience studies.
Yes, this biosignal processing approach handles respiratory (RSP) and muscular (EMG) signals. It cleans raw time-series data, detects relevant events or peaks, and computes domain-specific summaries alongside cardiovascular, neural, autonomic, and ocular data.
To epoch physiological data around specific stimuli, you apply event-locked segmentation to your raw time-series. This skill creates intervals around events to run stimulus-locked or resting analyses while computing consistent metrics across all epochs.
Limitations when computing complexity and entropy measures depend on raw signal quality and appropriate peak detection. While this skill applies advanced decomposition and filtering, accurate nonlinear metrics require clean time-series inputs free from heavy artifacts.
fuzzy-dynamicsAutomate biosignal preprocessing and analysis to extract physiological metrics from ECG, EEG, EDA, RSP, EMG, and EOG data.
pur3v4d3rProcess and analyze multichannel biosignals to extract HRV, EEG, EDA, and respiration metrics.
xjtulycDesign PsychoPy behavioral experiments with frame-timed stimulus presentation and BIDS events.tsv export.
Hung-3008Extract cardiovascular, neural, and autonomic biomarkers from biosignals using NeuroKit2.
