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pick-channels
Select channels by name list or type (EEG, MEG, etc.)
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Select channels by name list or type (EEG, MEG, etc.)
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Neurodata Without Borders (.nwb) — the de-facto standard for in-vivo electrophysiology (Neuropixels / AIBS / DANDI / IBL)
Index of L0 data-format loader skills by family
Index of all L2 paradigm skills by group + analysis_goal → paradigm matrix
Two-phase BCI data preprocessing pipeline: deep-inspect → plan → propose → user confirm → automated code/execute/QC/export
BIDS-iEEG sidecar (*_channels.tsv / *_electrodes.tsv / *_coordsystem.json / *_ieeg.json) — clinical sEEG / ECoG standard
Multiscale Electrophysiology Format v3 (Mayo Clinic) — long-duration encrypted sEEG
| name | pick_channels |
| description | Select channels by name list or type (EEG, MEG, etc.) |
| layer | L3 |
| group | channel |
| metadata | {"tags":["operator","channels","select","subset","pick"],"modalities":["eeg","seeg","ecog","meg"],"step_string":"pick_channels","analysis_goal_allowed":["classification","source_localization","feature_extraction","clinical_screening","exploratory","generic","connectivity","phase_amplitude_coupling","online_inference"],"analysis_goal_forbidden":[]} |
Subsets the data to include only specified channels. Can select by explicit channel names or by channel type (EEG, MEG, sEEG, etc.).
pick_channels:{selection}
Examples:
pick_channels:eeg — Keep only EEG-type channels (remove EOG, EMG, stim, etc.)pick_channels:Fp1,Fp2,F3,F4,C3,C4,P3,P4 — Explicit channel namespick_channels:meg — Keep only MEG channelspick_channels:seeg — Keep only sEEG channels| Parameter | Type | Description |
|---|---|---|
| selection | string | Channel type (eeg, meg, seeg, ecog, emg) OR comma-separated channel names |
When a single recognized type is given, channels are filtered by their ch_types metadata:
eeg — scalp EEG electrodesmeg — MEG sensorsseeg — sEEG depth contactsecog — ECoG grid/strip contactsemg — EMG channels (usually removed)data — all data channels (exclude stim/misc)| Goal | Selection | Notes |
|---|---|---|
| Remove stim channels | pick_channels:eeg | Keeps only EEG-type |
| Motor cortex subset | pick_channels:C3,Cz,C4,FC3,FC4,CP3,CP4 | MI BCI |
| Remove EOG after ICA | pick_channels:eeg | Apply after ICA step |
raw.pick_channels(['C3', 'Cz', 'C4'])
import numpy as np
pick_names = ['C3', 'Cz', 'C4']
pick_idx = [i for i, ch in enumerate(channels) if ch in pick_names]
data = data[pick_idx]
channels = [channels[i] for i in pick_idx]
# Select only EEG channels from mixed recording
ch_types = data_dict['meta']['ch_types'] # e.g. ['eeg','eeg','eog','stim',...]
indices = [i for i, t in enumerate(ch_types) if t == 'eeg']
data = data[indices]
channels = [channels[i] for i in indices]
raw.pick_channels(names) — MNE methodraw.pick_types(eeg=True) — alternative for type-based selection