eFEL: electrophysiology feature extraction library

Area of Science:

• Neuroscience
• Computational Biology
• Bioinformatics

Background:

• Electrophysiological recordings are crucial for understanding neuronal function and network dynamics.
• Challenges in data heterogeneity and software environments impede reproducibility and interoperability in electrophysiology analysis.
• A standardized framework is necessary for consistent, efficient, and portable analysis of electrophysiological data.

Purpose of the Study:

• To introduce the Electrophysiology Feature Extraction Library (eFEL) as a solution for standardized electrophysiological data analysis.
• To provide a versatile, open-source tool that ensures reproducibility and interoperability in neuroscience research.
• To facilitate the extraction of over 90 standardized electrophysiological features.

Main Methods:

• Developed eFEL as a cross-platform, open-source library with a C++ core and Python interface.
• Implemented standardized definitions for over 90 electrophysiological features.
• Integrated customizable feature dependencies, caching, parallelization, and compatibility with community standards (e.g., NWB).

Main Results:

• eFEL offers standardized definitions for more than 90 electrophysiological features.
• The library supports customizable feature dependencies, caching, and parallel processing.
• eFEL integrates with common electrophysiology formats and simulation environments, promoting FAIR data principles.

Conclusions:

• eFEL is a FAIR-compliant, versatile resource for reproducible electrophysiological data analysis.
• The library has been successfully applied in various studies, including single-cell analysis, model optimization, and circuit simulations.
• eFEL enhances interoperability and consistency in computational neuroscience research.