https://github.com/mne-tools/mne-denoise

mne-denoise provides narrow-band artefact removal tailored to MNE-Python workflows. It wraps harmonic regression techniques to suppress power-line noise and other oscillatory contaminants while preserving signal rank and interpretability.
https://github.com/mne-tools/mne-denoise

bci brain-computer-interface datacleaning dss eeg neuroscience noiseremoval python

Last synced: 6 days ago
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Host: GitHub
URL: https://github.com/mne-tools/mne-denoise
Owner: mne-tools
License: bsd-3-clause
Created: 2024-07-11T21:13:13.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2026-06-05T19:04:15.000Z (15 days ago)
Last Synced: 2026-06-05T20:22:06.512Z (15 days ago)
Topics: bci, brain-computer-interface, datacleaning, dss, eeg, neuroscience, noiseremoval, python
Language: Python
Homepage:
Size: 119 MB
Stars: 27
Watchers: 6
Forks: 8
Open Issues: 11
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

          # mne-denoise

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**Advanced denoising algorithms for M/EEG data in MNE-Python.**

`mne-denoise` provides powerful signal denoising techniques for the MNE-Python ecosystem, including **Denoising Source Separation (DSS)** and **ZapLine** algorithms. These methods excel at extracting signals of interest by exploiting data structure rather than just variance.

## Features

### DSS Module

- **Linear DSS**: Extract components based on reproducibility across trials or characteristic frequencies

- **Iterative DSS**: Powerful nonlinear separation for complex non-Gaussian sources

- **20+ Pluggable Denoisers**: Spectral, temporal, periodic, and ICA-style bias functions

- **Specialized Variants**: TSR, SSVEP enhancement, narrowband oscillation extraction

### ZapLine Module

- **ZapLine**: Efficient removal of power line noise (50/60 Hz) and harmonics

- **ZapLine-plus**: Fully adaptive mode with automatic frequency detection

- **Per-chunk Processing**: Handles non-stationary noise characteristics

- **Quality Assurance**: Built-in spectral checks to prevent over-cleaning

### Integration

- **MNE-Python**: Works directly with `Raw`, `Epochs`, and `Evoked` objects or `numpy` arrays.

- **Scikit-Learn API**: Standard `fit()`, `transform()`, `fit_transform()` interface

- **Visualization**: Built-in plotting for components and cleaning results

## Installation

### From PyPI (recommended)

```bash

pip install mne-denoise

```

### From source (development)

```bash

git clone https://github.com/mne-tools/mne-denoise.git

cd mne-denoise

pip install -e ".[dev]"

```

## Quick Start

### DSS: Enhancing Evoked Responses

DSS finds spatial filters that maximize the ratio of reproducible (evoked) to total power:

```python

import mne

from mne_denoise.dss import DSS, AverageBias

# Load your epoched data

epochs = mne.read_epochs("sample-epo.fif")

# Create DSS with trial-average bias

dss = DSS(bias=AverageBias(), n_components=5)

dss.fit(epochs)

# Option 1: Extract source time courses

sources = dss.transform(epochs)

# Option 2: Reconstruct denoised sensor data

cleaned_epochs = dss.transform(epochs, return_type="epochs")

```

### DSS: Extracting Oscillations

Isolate specific frequency bands (e.g., alpha rhythm):

```python

from mne_denoise.dss import DSS, BandpassBias

# Create bandpass bias for alpha (8-12 Hz)

bias = BandpassBias(sfreq=epochs.info["sfreq"], freq=10, bandwidth=4)

dss = DSS(bias=bias, n_components=3)

alpha_sources = dss.fit_transform(epochs)

```

### ZapLine: Removing Line Noise

Remove 50/60 Hz power line artifacts:

```python

import mne

from mne_denoise.zapline import ZapLine

# Load continuous data

raw = mne.io.read_raw_fif("sample-raw.fif", preload=True)

# Standard mode: specify line frequency

zapline = ZapLine(sfreq=raw.info["sfreq"], line_freq=50.0)

cleaned_data = zapline.fit_transform(raw)

# Adaptive mode: automatic detection and per-chunk processing

zapline_plus = ZapLine(

    sfreq=raw.info["sfreq"],

    line_freq=None,  # Auto-detect

    adaptive=True,

)

cleaned = zapline_plus.fit_transform(raw)

print(f"Detected line frequency: {zapline_plus.detected_freq_} Hz")

```

## Documentation

Full documentation is available at **[mne.tools/mne-denoise](https://mne.tools/mne-denoise/)**.

- [Getting Started Guide](https://mne.tools/mne-denoise/getting-started.html)

- [API Reference](https://mne.tools/mne-denoise/api.html)

- [Example Gallery](https://mne.tools/mne-denoise/auto_examples/index.html)

## 🏗️ Architecture

```

mne_denoise/

├── dss/                    # Denoising Source Separation

│   ├── linear.py           # Core DSS algorithm, DSS estimator

│   ├── nonlinear.py        # Iterative DSS, IterativeDSS estimator

│   ├── denoisers/          # 20+ pluggable bias functions

│   │   ├── spectral.py     # BandpassBias, LineNoiseBias

│   │   ├── temporal.py     # TimeShiftBias, SmoothingBias

│   │   ├── periodic.py     # CombFilterBias, PeakFilterBias

│   │   └── ...

│   └── variants/           # Pre-built applications

│       ├── tsr.py          # Time-Shift Repeatability

│       ├── ssvep.py        # SSVEP enhancement

│       └── narrowband.py   # Oscillation extraction

├── zapline/                # Line noise removal

│   ├── core.py             # ZapLine estimator

│   └── adaptive.py         # ZapLine-plus utilities

└── viz/                    # Visualization tools

```

## Testing

```bash

# Run tests

pytest

# With coverage

pytest --cov=mne_denoise --cov-report=html

```

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

```bash

# Development setup

git clone https://github.com//mne-denoise.git

cd mne-denoise

pip install -e ".[dev,docs]"

pre-commit install

```

## References

### DSS

> Särelä, J., & Valpola, H. (2005). Denoising source separation. _Journal of Machine Learning Research_, 6, 233-272.

> de Cheveigné, A., & Simon, J. Z. (2008). Denoising based on spatial filtering. _Journal of Neuroscience Methods_, 171(2), 331-339.

### ZapLine

> de Cheveigné, A. (2020). ZapLine: A simple and effective method to remove power line artifacts. _NeuroImage_, 207, 116356.

> Klug, M., & Kloosterman, N. A. (2022). Zapline-plus: A completely automatic and highly effective method for removing power line noise. _Human Brain Mapping_, 43(9), 2743-2758.

## License

BSD 3-Clause License. See [LICENSE](LICENSE) for details.

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