EAG_SPy: An Automated Python Toolkit for Electroantennogram (EAG) and Sensillar Potential (SP) Analysis
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Fecha
Publicador:
Consejo Nacional de Investigaciones Científicas y Técnicas
Fecha de depósito:
29/05/2026
Fecha de creación:
24/2/2026
Clasificación temática
Resumen
EAG-SPy is an open-source Python toolkit designed for the automated detection, filtering, and quantification of sensillar potentials (SPs) recorded in .abf (Axon Binary File) format. The software provides researchers with a complete pipeline from raw signal processing to statistical visualization.
Core capabilities:
• Filtering, automatic detection, and quantification of sensillar potentials (SPs)
• Individual sweep visualization and statistical summary
• Export of results in .csv and .txt formats
• Generation of annotated figures and histogram summaries.
EAG-SPy is distributed as a compiled Windows executable requiring no Python installation, and as open Python source code. It is designed to be accessible to researchers without programming experience while remaining fully transparent and reproducible for computational users.
Información Técnica
EAG_SPy is a custom-developed software tool for automated analysis of electroantennogram (EAG) and sensillar potential (SP) recordings. The software is implemented in Python and distributed as a standalone executable for Windows systems, requiring no external dependencies or Python installation. EAG_SPy performs latency-based SP detection, kinetic parameter extraction, linear regression analysis of response onset, and derivative-based characterization of membrane potential dynamics. The script performs:
• Gaussian low-pass filtering in the frequency domain (FFT-based)
• Automatic SP detection using a relative threshold criterion
• Quantification of amplitude, latency, duration, and kinetic parameters
• Half-rise and half-decay time computation (absolute and normalized)
• Area under the curve (AUC) measurements
• Linear regression analysis of depolarization slopes (50% and 80%)
• First derivative (dV/dt) analysis for kinetic characterization
• Automatic generation of publication-ready figures
• Structured export of results in .csv and .txt formats.
The latency detection approach is conceptually based on:
Pézier et al. (2007). Ca2+ Stabilizes the Membrane Potential of Moth Olfactory Receptor Neurons at Rest and Is Essential for Their Fast Repolarization." Chemical Senses 32: 305–317. (doi:10.1093/chemse/bjl059)
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EAG-SPy_Automated_Python_Toolkit__py_Description.txt
What does this script do? / User input / Parameters computed per sweep / etc.
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