TY - GEN
T1 - Robust Online Multiband Drift Estimation in Electrophysiology Data
AU - Windolf, Charlie
AU - Paulk, Angelique C.
AU - Kfir, Yoav
AU - Trautmann, Eric
AU - Meszéna, Domokos
AU - Muñoz, William
AU - Caprara, Irene
AU - Jamali, Mohsen
AU - Boussard, Julien
AU - Williams, Ziv M.
AU - Cash, Sydney S.
AU - Paninski, Liam
AU - Varol, Erdem
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 motion with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) in addition to spikes. Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, enabling the method to scale up to longer and higher-resolution recordings, and possibly facilitating real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and simple methods for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from human and mouse.
AB - 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 motion with four major contributions. First, we extend previous decentralized methods to use multiband information, leveraging the local field potential (LFP) in addition to spikes. Second, we show that the LFP-based approach enables registration at sub-second temporal resolution. Third, we introduce an efficient online motion tracking algorithm, enabling the method to scale up to longer and higher-resolution recordings, and possibly facilitating real-time applications. Finally, we improve the robustness of the approach by introducing a structure-aware objective and simple methods for adaptive parameter selection. Together, these advances enable fully automated scalable registration of challenging datasets from human and mouse.
KW - Decentralization
KW - electrophysiology
KW - online optimization
UR - http://www.scopus.com/inward/record.url?scp=85173280819&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85173280819&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10095487
DO - 10.1109/ICASSP49357.2023.10095487
M3 - Conference contribution
AN - SCOPUS:85173280819
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -