PASER

PASER processes extracellular neural recordings collected under switching magnetic fields by removing gradient and pulse artifacts to enable unit isolation and quantitative analysis.

Key Features:

• Automated denoising and artifact removal: Performs automated denoising and removes gradient and pulse artifacts induced by switching magnetic fields to enhance signal clarity.
• Quality control of electrical recordings: Implements measures to assess unit quality and recording fidelity for extracellular signals.
• Unit classification and visualization: Provides spike-sorted unit classification and visualization for interpretation of neural clusters.
• Modular MATLAB implementation and integration: Implemented in MATLAB with a modular architecture that supports integration with third-party applications for data import, spike sorting, and local field potential analysis.
• Spike sorting algorithm evaluation: Evaluates multiple spike sorting algorithms for computational efficiency, unit yield, unit quality, and clustering reliability under perturbations such as self-blurring and noise-reversal.
• KiloSort integration and automated pipeline: Integrates KiloSort as the default spike sorter and implements a fully automated pipeline for quantitative analysis of broadband extracellular signals.
• Compatibility with intracranial multichannel recordings and high-throughput: Supports multichannel intracranial electrode recordings independent of electrode count or recording duration to enable high-throughput analysis across electromagnetic recording conditions.

Scientific Applications:

• Multichannel intracranial recordings: Analysis of multichannel extracellular data from intracranial electrodes recorded in switching magnetic fields.
• Magnetic neuroimaging: Processing neural signals acquired during magnetic neuroimaging experiments involving pulsing or switching fields.
• Magnetogenetics: Analysis of neural activity in magnetogenetics experiments that involve magnetic field stimulation or control.
• High-throughput neural recording projects: Enables large-scale quantitative analysis of broadband extracellular signals across diverse electromagnetic recording conditions.

Methodology:

Evaluates spike sorting algorithms (including KiloSort) using metrics for computational efficiency, unit yield, unit quality, and clustering reliability under self-blurring and noise-reversal, and implements an automated MATLAB pipeline performing denoising, artifact removal, spike sorting, and local field potential analysis for broadband extracellular signals.