Ghostipy: An efficient signal processing and spectral analysis toolbox for large data

Abstract

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of chan-nels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large-scale modalities. Here we introduce GhostiPy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time–frequency transforms. GhostiPy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high-channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently en-countered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portabil-ity and scalability of neural data analysis.