Robust multiband drift estimation in electrophysiology data

High-density electrophysiology probes have opened new possibilities for systems neuroscience in human and non-human animals, but probe motion poses a challenge for downstream analyses, particularly in human recordings. We improve on the state of the art for tracking this drift with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) and current source density (CSD) as well as spikes. Second, we show that the CSD-based approach enables subsecond drift tracking. Third, we introduce an efficient online algorithm, enabling the method to scale to longer and higher-resolution recordings, which could facilitate real-time applications. Finally, we improve the robustness of the approach by accounting for the nonstationarities that occur in real data and by automating parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from both human and mouse.