Robust online 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 (or drift) while recording poses a challenge for downstream analyses, particularly in human recordings. Here, we improve on the state of the art for tracking this drift with four major contributions. First, we extend previous methods to multimodal data, leveraging the local field potential and current source density (CSD) in addition to spikes. Second, we show that the CSD-based approach enables registration at subsecond temporal resolution. Third, we introduce an efficient online motion tracking algorithm, which enables the method to scale up to longer and higher-resolution recordings, and could facilitate real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and a simple method for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from both human and mouse.