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Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging
Conference proceeding

Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging

Hung-Yun Lu, Anil Bollimunta, Ryan W Eaton, John H Morrison, Karen A Moxon, Jose M Carmena, Jonathan J Nassi, Samantha R Santacruz and IEEE
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), v 2021, pp 5860-5863
01 Nov 2021
PMID: 34892452

Abstract

Brain-computer interfaces Calcium Imaging Neurons Real-time systems Tracking Training
Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.

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Collaboration types
Domestic collaboration
Web of Science research areas
Engineering, Biomedical
Engineering, Electrical & Electronic
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