GhostiPy

GhostiPy provides high-performance signal processing and spectral analysis for large-scale, high-channel-count neural recordings in Python.

Key Features:

• High-Performance Algorithms: Implements signal processing and spectral analysis techniques, including optimal digital filters and time-frequency transforms.
• Parallelized, Blocked Computation: Employs parallelized algorithms with blocked computation to accelerate processing of large datasets.
• Out-of-Core Computation: Supports out-of-core computation to process datasets larger than available system memory.

Scientific Applications:

• Neural signal spectral analysis: Extracts spectral features and time-frequency representations from neural recordings to study brain activity.
• High-channel-count electrophysiology: Processes large multichannel recordings generated by modern high-channel-count neural recording technologies.
• Large-scale data processing: Enables preprocessing and analysis of datasets that exceed system memory constraints.

Methodology:

Implemented in Python; methods explicitly include optimal digital filters, time-frequency transforms, parallelized blocked algorithms, and out-of-core computation.