mne.datasets.sample.data_path#

mne.datasets.sample.data_path(path=None, force_update=False, update_path=True, download=True, *, verbose=None)[source]#

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. Default is False.

update_pathbool | None

If True (default), 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 | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
pathinstance of pathlib.Path

Path to sample dataset directory.

Examples using mne.datasets.sample.data_path#

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python
Modifying data in-place

Modifying data in-place

Modifying data in-place
Parsing events from raw data

Parsing events from raw data

Parsing events from raw data
The Info data structure

The Info data structure

The Info data structure
Working with sensor locations

Working with sensor locations

Working with sensor locations
Getting started with mne.Report

Getting started with mne.Report

Getting started with mne.Report
Importing data from fNIRS devices

Importing data from fNIRS devices

Importing data from fNIRS devices
The Raw data structure: continuous data

The Raw data structure: continuous data

The Raw data structure: continuous data
Working with events

Working with events

Working with events
Annotating continuous data

Annotating continuous data

Annotating continuous data
Built-in plotting methods for Raw objects

Built-in plotting methods for Raw objects

Built-in plotting methods for Raw objects
Overview of artifact detection

Overview of artifact detection

Overview of artifact detection
Handling bad channels

Handling bad channels

Handling bad channels
Rejecting bad data spans and breaks

Rejecting bad data spans and breaks

Rejecting bad data spans and breaks
Filtering and resampling data

Filtering and resampling data

Filtering and resampling data
Repairing artifacts with ICA

Repairing artifacts with ICA

Repairing artifacts with ICA
Background on projectors and projections

Background on projectors and projections

Background on projectors and projections
Repairing artifacts with SSP

Repairing artifacts with SSP

Repairing artifacts with SSP
Setting the EEG reference

Setting the EEG reference

Setting the EEG reference
Signal-space separation (SSS) and Maxwell filtering

Signal-space separation (SSS) and Maxwell filtering

Signal-space separation (SSS) and Maxwell filtering
Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data
The Epochs data structure: discontinuous data

The Epochs data structure: discontinuous data

The Epochs data structure: discontinuous data
Visualizing epoched data

Visualizing epoched data

Visualizing epoched data
Exporting Epochs to Pandas DataFrames

Exporting Epochs to Pandas DataFrames

Exporting Epochs to Pandas DataFrames
Divide continuous data into equally-spaced epochs

Divide continuous data into equally-spaced epochs

Divide continuous data into equally-spaced epochs
The Evoked data structure: evoked/averaged data

The Evoked data structure: evoked/averaged data

The Evoked data structure: evoked/averaged data
Visualizing Evoked data

Visualizing Evoked data

Visualizing Evoked data
EEG analysis - Event-Related Potentials (ERPs)

EEG analysis - Event-Related Potentials (ERPs)

EEG analysis - Event-Related Potentials (ERPs)
Plotting whitened data

Plotting whitened data

Plotting whitened data
FreeSurfer MRI reconstruction

FreeSurfer MRI reconstruction

FreeSurfer MRI reconstruction
Source alignment and coordinate frames

Source alignment and coordinate frames

Source alignment and coordinate frames
Using an automated approach to coregistration

Using an automated approach to coregistration

Using an automated approach to coregistration
Head model and forward computation

Head model and forward computation

Head model and forward computation
How MNE uses FreeSurfer's outputs

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs
Fixing BEM and head surfaces

Fixing BEM and head surfaces

Fixing BEM and head surfaces
Computing a covariance matrix

Computing a covariance matrix

Computing a covariance matrix
The SourceEstimate data structure

The SourceEstimate data structure

The SourceEstimate data structure
Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit
Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization
Computing various MNE solutions

Computing various MNE solutions

Computing various MNE solutions
Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer
Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI
Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power

Non-parametric between conditions cluster statistic on single trial power
Spatiotemporal permutation F-test on full sensor data

Spatiotemporal permutation F-test on full sensor data

Spatiotemporal permutation F-test on full sensor data
Permutation t-test on source data with spatio-temporal clustering

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

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

2 samples permutation 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

Repeated measures ANOVA 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

Mass-univariate twoway repeated measures ANOVA on single trial power

Mass-univariate twoway repeated measures ANOVA on single trial power
Decoding (MVPA)

Decoding (MVPA)

Decoding (MVPA)
Locating intracranial electrode contacts

Locating intracranial electrode contacts

Locating intracranial electrode contacts
Working with sEEG data

Working with sEEG data

Working with sEEG data
Working with ECoG data

Working with ECoG data

Working with ECoG data
Corrupt known signal with point spread

Corrupt known signal with point spread

Corrupt known signal with point spread
DICS for power mapping

DICS for power mapping

DICS for power mapping
Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix
Generate simulated evoked data

Generate simulated evoked data

Generate simulated evoked data
Generate simulated raw data

Generate simulated raw data

Generate simulated raw data
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Generate simulated source data

Generate simulated source data

Generate simulated source data
Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals
Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response
Transform EEG data using current source density (CSD)

Transform EEG data using current source density (CSD)

Transform EEG data using current source density (CSD)
Show EOG artifact timing

Show EOG artifact timing

Show EOG artifact timing
Compare the different ICA algorithms in MNE

Compare the different ICA algorithms in MNE

Compare the different ICA algorithms in MNE
Interpolate bad channels for MEG/EEG channels

Interpolate bad channels for MEG/EEG channels

Interpolate bad channels for MEG/EEG channels
Removing muscle ICA components

Removing muscle ICA components

Removing muscle ICA components
Shifting time-scale in evoked data

Shifting time-scale in evoked data

Shifting time-scale in evoked data
Remap MEG channel types

Remap MEG channel types

Remap MEG channel types
XDAWN Denoising

XDAWN Denoising

XDAWN Denoising
Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
Visualize channel over epochs as an image

Visualize channel over epochs as an image

Visualize channel over epochs as an image
Plotting EEG sensors on the scalp

Plotting EEG sensors on the scalp

Plotting EEG sensors on the scalp
Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data
Plotting topographic maps of evoked data

Plotting topographic maps of evoked data

Plotting topographic maps of evoked data
Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance
Plotting sensor layouts of MEG systems

Plotting sensor layouts of MEG systems

Plotting sensor layouts of MEG systems
Plot the MNE brain and helmet

Plot the MNE brain and helmet

Plot the MNE brain and helmet
Plot a cortical parcellation

Plot a cortical parcellation

Plot a cortical parcellation
Make figures more publication ready

Make figures more publication ready

Make figures more publication ready
Show noise levels from empty room data

Show noise levels from empty room data

Show noise levels from empty room data
Sensitivity map of SSP projections

Sensitivity map of SSP projections

Sensitivity map of SSP projections
Compare evoked responses for different conditions

Compare evoked responses for different conditions

Compare evoked responses for different conditions
Plot custom topographies for MEG sensors

Plot custom topographies for MEG sensors

Plot custom topographies for MEG sensors
Cross-hemisphere comparison

Cross-hemisphere comparison

Cross-hemisphere comparison
Compute a cross-spectral density (CSD) matrix

Compute a cross-spectral density (CSD) matrix

Compute a cross-spectral density (CSD) matrix
Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs
Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space
Compute source power spectral density (PSD) in a label

Compute source power spectral density (PSD) in a label

Compute source power spectral density (PSD) in a label
Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM
Temporal whitening with AR model

Temporal whitening with AR model

Temporal whitening with AR model
Permutation F-test on sensor data with 1D cluster level

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

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

FDR correction on T-test on sensor data

FDR correction on T-test on sensor data
Regression on continuous data (rER[P/F])

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

Regression on continuous data (rER[P/F])
Permutation T-test on sensor data

Permutation T-test on sensor data

Permutation T-test on sensor data
Decoding source space data

Decoding source space data

Decoding source space data
Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions
Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques
XDAWN Decoding From EEG data

XDAWN Decoding From EEG data

XDAWN Decoding From EEG data
Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)
Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters
Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors
Generate a left cerebellum volume source space

Generate a left cerebellum volume source space

Generate a left cerebellum volume source space
Use source space morphing

Use source space morphing

Use source space morphing
Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs
Compute sLORETA inverse solution on raw data

Compute sLORETA inverse solution on raw data

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

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

Compute MNE-dSPM inverse solution on evoked data in volume source space
Source localization with a custom inverse solver

Source localization with a custom inverse solver

Source localization with a custom inverse solver
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

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

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Extracting time course from source_estimate object

Extracting time course from source_estimate object

Extracting time course from source_estimate object
Generate a functional label from source estimates

Generate a functional label from source estimates

Generate a functional label from source estimates
Extracting the time series of activations in a label

Extracting the time series of activations in a label

Extracting the time series of activations in a label
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space

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

Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE
Morph surface source estimate

Morph surface source estimate

Morph surface source estimate
Morph volumetric source estimate

Morph volumetric source estimate

Morph volumetric source estimate
Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model
Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

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

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
Compute Rap-Music on evoked data

Compute Rap-Music on evoked data

Compute Rap-Music on evoked data
Reading an inverse operator

Reading an inverse operator

Reading an inverse operator
Reading an STC file

Reading an STC file

Reading an STC file
Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG
Estimate data SNR using an inverse

Estimate data SNR using an inverse

Estimate data SNR using an inverse
Computing source space SNR

Computing source space SNR

Computing source space SNR
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution