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scientific-neuroscience-electrophysiology

Build reproducible neuroscience pipelines for spike sorting and ERP/HRV/EDA analyses.

3|1|Updated Feb 11, 2026
Search Tags:#neuroscience#connectivity#erp#electrophysiology#hrv#spike_sorting
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-neuroscience-electrophysiology
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-neuroscience-electrophysiology
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-neuroscience-electrophysiology
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-neuroscience-electrophysiology

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Neuroscience researchers often need an integrated, reproducible workflow to process multi-modal neural data—from spike sorting and signal quality assessment to ERP, HRV, EDA, EMG analyses, and connectivity studies—for consistent, publication-ready insights.

Core Features & Use Cases

  • End-to-end pipelines that combine SpikeInterface-based spike sorting, quality metrics (SNR, ISI violations), ERP extraction with MNE-Python, HRV analysis with NeuroKit2, EDA and EMG processing, and functional connectivity estimation.
  • Multi-modal support for Neuropixels/MEA electrophysiology, EEG/ERP, ECG HRV, and autonomic measures in unified workflows.
  • Use Case: Reuse established neuroscience analysis patterns to transform raw neural and physiological data into comparable results across experiments.

Quick Start

Create a reproducible neuroscience pipeline that loads electrophysiology and physiological data, runs spike sorting and ERP/HRV/EDA/EMG analyses, and exports a unified report.

Frequently Asked Questions about scientific-neuroscience-electrophysiology

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a reproducible electrophysiology pipeline for spike sorting and ERP analysis?▼

Build an electrophysiology pipeline by integrating SpikeInterface for spike sorting, MNE-Python for ERP extraction, and NeuroKit2 for HRV/EDA/EMG processing to generate reproducible, publication-ready neural and physiological data workflows.

Can I process Neuropixels and EEG data in the same multimodal neuroscience workflow?▼

Yes, multimodal neuroscience workflows support Neuropixels/MEA electrophysiology, EEG/ERP, ECG HRV, and autonomic measures in unified pipelines to transform raw neural and physiological data into comparable results across experiments.

What signal quality metrics are included in a spike sorting pipeline?▼

A spike sorting pipeline includes signal quality assessment metrics such as signal-to-noise ratio (SNR) and interspike interval (ISI) violations to ensure robust, reproducible electrophysiology data processing and reliable downstream analysis.

Does this neuroscience pipeline support connectivity metrics and MNE-Python ecosystems?▼

Yes, the neuroscience pipeline supports functional connectivity estimation and ensures compatibility with MNE-Python, SpikeInterface, and NeuroKit2 ecosystems to unify ERP, HRV, EDA, and ECG analyses within reproducible workflows.

How do I extract HRV and EDA physiological signals alongside EEG data?▼

Extract HRV and EDA physiological signals alongside EEG data by applying NeuroKit2 for autonomic measures and MNE-Python for ERP extraction within an end-to-end multimodal neuroscience pipeline.

What's the best way to unify spike sorting and ECG analyses for publication-ready results?▼

Unify spike sorting and ECG analyses by applying an end-to-end neuroscience pipeline that combines SpikeInterface, MNE-Python, and NeuroKit2 to process multi-modal neural data into consistent, publication-ready insights.

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