`/neuroflow:data-preprocess`¶

Build and run a preprocessing pipeline on your raw data.

/data-preprocess guides you through creating and running a preprocessing pipeline: from loading raw data to cleaned, epoched, and quality-checked datasets ready for analysis.

When to use it¶

After /data — you have inventoried and validated your raw data
You need to filter, re-reference, run ICA, or epoch your EEG data
You want a documented preprocessing config and QC report

What it does¶

Claude asks:

What modality? (EEG, iEEG, fMRI, other)
Where is the data?
What preprocessing steps are needed? Or should it apply a standard pipeline for the modality?

Standard EEG pipeline¶

For EEG data, Claude follows this pipeline using MNE-Python:

Step	What happens
1. Load	`mne.io.read_raw_*()` — supports FIF, BrainVision, EDF, EEGLAB
2. Filter	Bandpass (e.g. 1–40 Hz) + notch (50 Hz or 60 Hz)
3. Re-reference	Average reference, linked mastoids, or REST
4. Bad channels	Mark bad channels manually or via automated detection
5. ICA	Fit ICA, inspect components, remove EOG and ECG artifacts
6. Interpolate	Interpolate marked bad channels using spherical spline
7. Epoch	Create epochs around event markers
8. Baseline	Apply baseline correction
9. Reject	Reject epochs above amplitude threshold
10. Save	Save preprocessed epochs as `.fif`

Custom pipelines

Not using EEG? Just tell Claude your modality and what steps you need — it adapts the pipeline to fMRI (nilearn), iEEG, eye tracking, or any other modality.

Example session¶

/neuroflow:data-preprocess

Claude: I see from your project config that you're working with EEG data
        (BrainProducts, 64 channels). Data is in data/bids/.

        Should I apply the standard EEG preprocessing pipeline?

You: Yes, with these parameters: bandpass 0.5–45 Hz, notch 50 Hz, 
     average reference, ICA for ocular artifacts, epochs -200 to 800ms 
     around stimulus onset.

Claude: Parameters noted. I'll write a preprocessing script:

        scripts/preprocessing/preprocess.py
        ─────────────────────────────────────
        - Load: MNE read_raw_brainvision
        - Filter: 0.5–45 Hz bandpass, 50 Hz notch
        - Reference: average
        - ICA: FastICA, 40 components, EOG detection
        - Epochs: -0.2 to 0.8s, stimulus event codes [1, 2]
        - Rejection: peak-to-peak > 150 µV

        [generates the script and preprocess-config.md]

Output¶

File	Where	What it contains
`preprocess.py` (or equivalent)	`scripts/preprocessing/`	The preprocessing pipeline code
`preprocess-config.md`	`.neuroflow/data-preprocess/`	All parameters used
`preprocess-report.md`	`.neuroflow/data-preprocess/`	QC results: bad channels, ICA components removed, epoch rejection rates, per-subject notes

Files read and written¶

Direction	Files
Reads	`.neuroflow/project_config.md`, `.neuroflow/flow.md`, `.neuroflow/data/flow.md`, `.neuroflow/data-preprocess/flow.md`
Writes	`.neuroflow/data-preprocess/`, `.neuroflow/data-preprocess/flow.md`, `.neuroflow/sessions/YYYY-MM-DD.md`, `scripts/preprocessing/` (code output)

/data — get your data organized and converted first
/data-analyze — the next step after preprocessing

/neuroflow:data-preprocess¶