Get path to local copy of sample dataset.
None
| 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 update of the sample dataset even if a local copy exists. Default is False.
None
If True (default), set the MNE_DATASETS_SAMPLE_PATH
in mne-python
config to the given path. If None, the user is prompted.
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.
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.
pathlib.Path
Path to sample dataset directory.
mne.datasets.sample.data_path
#Overview of MEG/EEG analysis with MNE-Python
Getting started with mne.Report
Importing data from fNIRS devices
The Raw data structure: continuous data
Built-in plotting methods for Raw objects
Overview of artifact detection
Rejecting bad data spans and breaks
Background on projectors and projections
Signal-space separation (SSS) and Maxwell filtering
Preprocessing functional near-infrared spectroscopy (fNIRS) data
The Epochs data structure: discontinuous data
Regression-based baseline correction
Exporting Epochs to Pandas DataFrames
Divide continuous data into equally-spaced epochs
The Evoked data structure: evoked/averaged data
EEG analysis - Event-Related Potentials (ERPs)
Source alignment and coordinate frames
Using an automated approach to coregistration
Head model and forward computation
How MNE uses FreeSurfer’s outputs
The SourceEstimate data structure
Source localization with equivalent current dipole (ECD) fit
Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization
Computing various MNE solutions
Source reconstruction using an LCMV beamformer
Visualize source time courses (stcs)
EEG source localization given electrode locations on an MRI
Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power
Mass-univariate twoway repeated measures ANOVA 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
Locating intracranial electrode contacts
Corrupt known signal with point spread
Reading/Writing a noise covariance matrix
Generate simulated evoked data
Simulate raw data using subject anatomy
Generate simulated source data
Cortical Signal Suppression (CSS) for removal of cortical signals
Define target events based on time lag, plot evoked response
Transform EEG data using current source density (CSD)
Compare the different ICA algorithms in MNE
Interpolate bad channels for MEG/EEG channels
Removing muscle ICA components
Shifting time-scale in evoked data
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
Plotting sensor layouts of MEG systems
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
Compute a cross-spectral density (CSD) matrix
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 sensor space data with generalization across time and conditions
Analysis of evoked response using ICA and PCA reduction techniques
Compute effect-matched-spatial filtering (EMS)
Linear classifier on sensor data with plot patterns and filters
Display sensitivity maps for EEG and MEG sensors
Generate a left cerebellum volume source space
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 with a mixed source space
Compute source power estimate by projecting the covariance with MNE
Morph volumetric source estimate
Computing source timecourses with an XFit-like multi-dipole model
Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Compute Rap-Music on evoked data
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Estimate data SNR using an inverse
Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution