To help you get started, we've collected the most common ways that mne is being used within popular public projects.

mne-tools / mne-python / mne / time_frequency / psd.py View on Github

-------
    psds : ndarray, shape (..., n_freqs) or (..., n_freqs, n_segments)
        The power spectral densities. If ``average='mean`` or
        ``average='median'``, the returned array will have the same shape
        as the input data plus an additional frequency dimension.
        If ``average=None``, the returned array will have the same shape as
        the input data plus two additional dimensions corresponding to
        frequencies and the unaggregated segments, respectively.
    freqs : ndarray, shape (n_freqs,)
        The frequencies.

    Notes
    -----
    .. versionadded:: 0.14.0
    """
    _check_option('average', average, (None, 'mean', 'median'))

    dshape = x.shape[:-1]
    n_times = x.shape[-1]
    x = x.reshape(-1, n_times)

    # Prep the PSD
    n_fft, n_per_seg, n_overlap = _check_nfft(n_times, n_fft, n_per_seg,
                                              n_overlap)
    win_size = n_fft / float(sfreq)
    logger.info("Effective window size : %0.3f (s)" % win_size)
    freqs = np.arange(n_fft // 2 + 1, dtype=float) * (sfreq / n_fft)
    freq_mask = (freqs >= fmin) & (freqs <= fmax)
    freqs = freqs[freq_mask]

    # Parallelize across first N-1 dimensions
    x_splits = np.array_split(x, n_jobs)

View more ways to use mne

MNE-Python

MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.

Documentation

Documentation for MNE-Python encompasses installation instructions, tutorials, and examples for a wide variety of topics, contributing guidelines, and an API reference.

Forum

The user forum is the best place to ask questions about MNE-Python usage or the contribution process. The forum also features job opportunities and other announcements.

If you find a bug or have an idea for a new feature that should be added to MNE-Python, please use the issue tracker of our GitHub repository.

Installation

To install the latest stable version of MNE-Python with minimal dependencies only, use pip in a terminal:

$ pip install --upgrade mne

The current MNE-Python release requires Python 3.9 or higher. MNE-Python 0.17 was the last release to support Python 2.7.

For more complete instructions, including our standalone installers and more advanced installation methods, please refer to the installation guide.

Get the development version

To install the latest development version of MNE-Python using pip, open a terminal and type:

$ pip install --upgrade https://github.com/mne-tools/mne-python/archive/refs/heads/main.zip

To clone the repository with git, open a terminal and type:

$ git clone https://github.com/mne-tools/mne-python.git

Dependencies

The minimum required dependencies to run MNE-Python are:

• Python ≥ 3.9
• NumPy ≥ 1.23
• SciPy ≥ 1.9
• Matplotlib ≥ 3.6
• Pooch ≥ 1.5
• tqdm
• Jinja2
• decorator
• lazy_loader

For full functionality, some functions require:

• scikit-learn ≥ 1.1
• Joblib ≥ 0.15 (for parallelization)
• mne-qt-browser ≥ 0.5 (for fast raw data visualization)
• Qt ≥ 5.15 via one of the following bindings (for fast raw data visualization and interactive 3D visualization):
  • PySide6 ≥ 6.0
  • PyQt6 ≥ 6.0
  • PyQt5 ≥ 5.15
• Numba ≥ 0.54.0
• NiBabel ≥ 3.2.1
• OpenMEEG ≥ 2.5.6
• pandas ≥ 1.3.2
• Picard ≥ 0.3
• CuPy ≥ 9.0.0 (for NVIDIA CUDA acceleration)
• DIPY ≥ 1.4.0
• imageio ≥ 2.8.0
• PyVista ≥ 0.32 (for 3D visualization)
• PyVistaQt ≥ 0.4 (for 3D visualization)
• mffpy ≥ 0.5.7
• h5py
• h5io
• pymatreader

Contributing

Please see the contributing guidelines on our documentation website.

About

CI
Package
Docs
Meta

License

MNE-Python is licensed under the BSD-3-Clause license.