filtering skillA
filtering is agent-read markdown (skill) from cuhk-aim-group/neurodiscovery: Use this model doc whenever the user wants to perform neuroimaging signal denoising with classical temporal filtering methods. This is a non-deep-learning preprocessing route focused on temporal cleaning, frequency selection, and preparation of cleaner time series for downstream analysis..
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What the file says
# Filtering Model Doc ## Overview Filtering is a classical non-deep-learning method for neuroimaging signal denoising. - Model family: non-deep-learning preprocessing and denoising method - Typical objectives: - remove unwanted frequency content from BOLD time series - retain frequency bands relevant to resting-state or task analysis - prepare cleaner voxel-wise or ROI-wise time series for downstream connectivity, decoding, or statistical analysis - Primary input: preprocessed fMRI time series, optional confounds, optional mask, TR - Primary output: denoised BOLD image, cleaned ROI time series, optional QC summaries In NeuroClaw, this document is model-level guidance for temporal filtering workflows rather than predictive modeling. Upstream preparation should usually be delegated to: - `fmri-skill` for modality-level denoising planning and validated preprocessing sequences - `nilearn-tool` for concrete filtering and cleaned image export **Research use only.** --- ## Quick Start ### 1) Prepare denoising inputs Expected inputs: - preprocessed BOLD image - repetition time (`TR`) - optional confounds TSV - optional brain mask - optional requested frequency band …
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How to install
mdr add cuhk-aim-group/neurodiscovery/filtering@git:20260505.e9ddd22mdr add cuhk-aim-group/neurodiscovery/filtering@sha256:5614849add582120Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
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Source
cuhk-aim-group/neurodiscovery · 91 stars · license MIT · pushed 2026-09-22 · branch main
API
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