ica

name	ica
description	ICA artifact removal — automatic detection and exclusion of EOG/ECG components
layer	L3
group	adaptive_cleaning
metadata	{"tags":["operator","artifact","ica","eog","ecg","decomposition"],"modalities":["eeg","meg"],"step_string":"ica","analysis_goal_allowed":["classification","source_localization","feature_extraction","clinical_screening","exploratory","generic","connectivity"],"analysis_goal_forbidden":["online_inference"]}

ICA (Independent Component Analysis)

Function

Decomposes signal into independent components using FastICA, then automatically identifies and removes artifact components (eye blinks, heartbeat) by correlating with EOG/ECG reference channels.

Parameter Format

ica or ica:{artifact_types}

Examples:

• ica — Default: detect and remove EOG components only
• ica:eog — Same as default
• ica:eog,ecg — Remove both eye and cardiac artifacts
• ica:ecg — Remove cardiac artifacts only

Parameters

Parameter	Type	Default	Description
artifact_types	comma-separated	eog	Which artifact types to detect: eog, ecg

Internal parameters (not user-configurable):

• n_components: min(n_channels - 1, 25)
• method: FastICA
• random_state: 42 (EasyBCI global EASYBCI_SEED — keep fixed for reproducibility)
• max_iter: 500

When to Use

• Frequent eye blinks visible as high-amplitude frontal deflections
• Cardiac artifact (QRS complex) visible in EEG, especially with few channels
• Data has dedicated EOG/ECG reference channels for correlation-based detection
• Need to preserve data dimensionality (unlike drop_bads which removes channels)

When NOT to Use

• Fewer than 3 channels (ICA needs >= 3, practically >= 8 for good decomposition)
• Very short recordings (< 60 seconds) — insufficient data for stable decomposition
• sEEG/ECoG data (minimal eye/muscle artifacts due to intracranial placement)
• Spike data

Constraints

• Requires n_channels >= 3 (skipped otherwise with warning)
• Works best with n_channels >= 16 for meaningful decomposition
• Should be applied AFTER bandpass filtering (avoids slow drifts dominating components)
• Should be applied AFTER notch (line noise creates artificial components)
• Data should NOT be resampled to very low rates before ICA (need temporal resolution)

Ordering

• Apply AFTER: notch, bandpass
• Apply BEFORE: resample, scale, segmentation
• Reason: ICA needs full temporal resolution and clean frequency content to separate sources

Common Issues

• No components excluded: If no artifact components are detected, the data passes through unchanged. This may mean: no reference channels available, artifacts are mild, or n_components is too low.
• Too many components excluded: If > 3 components are excluded, consider whether the data has severe artifacts that ICA cannot cleanly separate. Manual inspection may be needed.
• Slow execution: ICA on high-channel-count data (> 64 ch) can be slow. Consider PCA dimensionality reduction first (not yet implemented as a step).

Reference Code

Standalone Implementation

import mne
from mne.preprocessing import ICA

info = mne.create_info(ch_names, sfreq, ch_types='eeg')
raw = mne.io.RawArray(data, info, verbose=False)

n_components = min(len(ch_names) - 1, 25)
ica = ICA(n_components=n_components, method='fastica', random_state=42, max_iter=500)
ica.fit(raw, verbose=False)

# Auto-detect EOG artifacts
eog_indices, _ = ica.find_bads_eog(raw, verbose=False)
# Auto-detect ECG artifacts
ecg_indices, _ = ica.find_bads_ecg(raw, verbose=False)

ica.exclude = list(set(eog_indices + ecg_indices))
ica.apply(raw, verbose=False)
data = raw.get_data().astype(np.float32)

Key API

• ICA(n_components=N, method='fastica', random_state=42, max_iter=500)
• ica.fit(raw, verbose=False)
• ica.find_bads_eog(raw) → (indices, scores)
• ica.find_bads_ecg(raw) → (indices, scores)
• ica.exclude = [...]; ica.apply(raw)