mne.datasets.sample.data_path

mne.datasets.sample.data_path(path=None, force_update=False, update_path=True, download=True, verbose=None)

Get path to local copy of sample dataset.

Parameters

pathNone | str

Location of where to look for the sample dataset. If None, the environment variable or config parameter MNE_DATASETS_SAMPLE_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the sample dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

force_updatebool

Force update of the sample dataset even if a local copy exists.

update_pathbool | None

If True, set the MNE_DATASETS_SAMPLE_PATH in mne-python config to the given path. If None, the user is prompted.

downloadbool

If False and the sample dataset has not been downloaded yet, it will not be downloaded and the path will be returned as ‘’ (empty string). This is mostly used for debugging purposes and can be safely ignored by most users.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

pathstr

Path to sample dataset directory.

Examples using mne.datasets.sample.data_path

Reading and writing raw files

Reading epochs from a raw FIF file

Reading an event file

Reading and writing an evoked file

Reading/Writing a noise covariance matrix

Read and visualize projections (SSP and other)

Generate simulated evoked data

Generate simulated raw data

Simulate raw data using subject anatomy

Generate simulated source data

Define target events based on time lag, plot evoked response

Transform EEG data using current source density (CSD)

Show EOG artifact timing

Find ECG artifacts

Find EOG artifacts

Compare the different ICA algorithms in MNE

Interpolate bad channels for MEG/EEG channels

Querying epochs with rich metadata

Re-referencing the EEG signal

Resampling data

Compute ICA components on epochs

Shifting time-scale in evoked data

Remap MEG channel types

XDAWN Denoising

How to convert 3D electrode positions to a 2D image.

Visualize channel over epochs as an image

Plotting EEG sensors on the scalp

Plotting topographic arrowmaps of evoked data

Plotting topographic maps of evoked data

Whitening evoked data with a noise covariance

Make an MNE-Report with a Slider

Plotting sensor layouts of MEG systems

Plot the MNE brain and helmet

Plot a cortical parcellation

Make figures more publication ready

Show noise levels from empty room data

Sensitivity map of SSP projections

Compare evoked responses for different conditions

Plot custom topographies for MEG sensors

Cross-hemisphere comparison

Compute a cross-spectral density (CSD) matrix

Compute the power spectral density of raw data

Compute Power Spectral Density of inverse solution from single epochs

Compute power and phase lock in label of the source space

Compute source power spectral density (PSD) in a label

Compute induced power in the source space with dSPM

Temporal whitening with AR model

Permutation F-test on sensor data with 1D cluster level

FDR correction on T-test on sensor data

Regression on continuous data (rER[P/F])

Permutation T-test on sensor data

Decoding source space data

Decoding sensor space data with generalization across time and conditions

Analysis of evoked response using ICA and PCA reduction techniques

XDAWN Decoding From EEG data

Compute effect-matched-spatial filtering (EMS)

Linear classifier on sensor data with plot patterns and filters

Compute seed-based time-frequency connectivity in sensor space

Compute mixed source space connectivity and visualize it using a circular graph

Compute coherence in source space using a MNE inverse solution

Compute full spectrum source space connectivity between labels

Compute source space connectivity and visualize it using a circular graph

Compute Phase Slope Index (PSI) in source space for a visual stimulus

Compute all-to-all connectivity in sensor space

Display sensitivity maps for EEG and MEG sensors

Generate a left cerebellum volume source space

Use source space morphing

Compute MNE-dSPM inverse solution on single epochs

Compute sLORETA inverse solution on raw data

Compute MNE-dSPM inverse solution on evoked data in volume source space

Source localization with a custom inverse solver

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Extracting time course from source_estimate object

Generate a functional label from source estimates

Extracting the time series of activations in a label

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute MNE inverse solution on evoked data in a mixed source space

Compute source power estimate by projecting the covariance with MNE

Morph surface source estimate

Morph volumetric source estimate

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Compute cross-talk functions for LCMV beamformers

Compute Rap-Music on evoked data

Reading an inverse operator

Reading an STC file

Compute spatial resolution metrics in source space

Compute spatial resolution metrics to compare MEG with EEG+MEG

Estimate data SNR using an inverse

Computing source space SNR

Time-frequency beamforming using DICS

Compute MxNE with time-frequency sparse prior

Plotting the full vector-valued MNE solution

Overview of MEG/EEG analysis with MNE-Python

Parsing events from raw data

The Info data structure

Working with sensor locations

The Raw data structure: continuous data

Working with events

Annotating continuous data

Built-in plotting methods for Raw objects

Overview of artifact detection

Interpolating bad channels

Rejecting bad data spans

Filtering and resampling data

Repairing artifacts with ICA

Background on projectors and projections

Repairing artifacts with SSP

Setting the EEG reference

Signal-space separation (SSS) and Maxwell filtering

Preprocessing functional near-infrared spectroscopy (fNIRS) data

The Epochs data structure: discontinuous data

Visualizing epoched data

Exporting Epochs to Pandas DataFrames

The Evoked data structure: evoked/averaged data

Visualizing Evoked data

EEG processing and Event Related Potentials (ERPs)

Plotting whitened data

FreeSurfer MRI reconstruction

How MNE uses FreeSurfer’s outputs

Source reconstruction using an LCMV beamformer

Computing a covariance matrix

Source localization with equivalent current dipole (ECD) fit

The role of dipole orientations in distributed source localization

EEG source localization given electrode locations on an MRI

Editing BEM surfaces in Blender

Head model and forward computation

Source localization with MNE/dSPM/sLORETA/eLORETA

Computing various MNE solutions

The SourceEstimate data structure

Source alignment and coordinate frames

Visualize source time courses (stcs)

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Spatiotemporal permutation F-test on full sensor data

Permutation t-test on source data with spatio-temporal clustering

2 samples permutation test on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Mass-univariate twoway repeated measures ANOVA on single trial power

Decoding (MVPA)

DICS for power mapping

Corrupt known signal with point spread

Working with ECoG data

Modifying data in-place

Getting started with mne.Report