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LorenFrankLab / spyglass

Neuroscience data analysis framework for reproducible research built by Loren Frank Lab at UCSF
https://lorenfranklab.github.io/spyglass/
MIT License
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artifact detection for clusterless decoding #805

Closed MichaelCoulter closed 9 months ago

MichaelCoulter commented 9 months ago

it would be nice to have an easy to use way to exclude artifact times before clusterless decoding. also general question: what is the best approach for artifact detection and removal for clusterless decoding.

edeno commented 9 months ago

Current option is to do this with the encoding and decoding intervals, but it might make more sense to incorporate the artifact detection from spike sorting into the feature detection step.

edeno commented 9 months ago

Hi @MichaelCoulter,

After talking to @khl02007 and looking at the code, it looks like artifacts are detected and removed the same as in the normal spikesorting. So, any threshold you wish to specify can happen via the ArtifactDetection and ArtifactRemovedIntervalList tables in the spikesorting pipeline. Any larger group events you wish to exclude can be further excluded in the encoding model interval list.