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:

path : 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 : bool

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

update_path : bool | None

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

download : bool

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.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose()).

Returns:

path : str

Path to sample dataset directory.

Examples using `mne.datasets.sample.data_path`¶

../_images/sphx_glr_plot_cwt_sensor_connectivity_thumb.png

Compute seed based time-frequency connectivity in sensor space

../_images/sphx_glr_plot_mixed_source_space_connectity_thumb.png

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

../_images/sphx_glr_plot_mne_inverse_coherence_epochs_thumb.png

Compute coherence in source space using a MNE inverse solution

../_images/sphx_glr_plot_mne_inverse_connectivity_spectrum_thumb.png

Compute full spectrum source space connectivity between labels

../_images/sphx_glr_plot_mne_inverse_label_connectivity_thumb.png

Compute source space connectivity and visualize it using a circular graph

../_images/sphx_glr_plot_mne_inverse_psi_visual_thumb.png

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

../_images/sphx_glr_plot_sensor_connectivity_thumb.png

Compute all-to-all connectivity in sensor space

../_images/sphx_glr_plot_decoding_csp_space_thumb.png

Decoding in sensor space data using the Common Spatial Pattern (CSP)

../_images/sphx_glr_plot_decoding_spatio_temporal_source_thumb.png

Decoding source space data

../_images/sphx_glr_plot_decoding_time_generalization_conditions_thumb.png

Decoding sensor space data with generalization across time and conditions

../_images/sphx_glr_plot_decoding_unsupervised_spatial_filter_thumb.png

Analysis of evoked response using ICA and PCA reduction techniques

../_images/sphx_glr_plot_decoding_xdawn_eeg_thumb.png

XDAWN Decoding From EEG data

../_images/sphx_glr_plot_ems_filtering_thumb.png

Compute effect-matched-spatial filtering (EMS)

../_images/sphx_glr_plot_linear_model_patterns_thumb.png

Linear classifier on sensor data with plot patterns and filters

../_images/sphx_glr_plot_decimate_head_surface_thumb.png

Decimating scalp surface

../_images/sphx_glr_plot_forward_sensitivity_maps_thumb.png

Display sensitivity maps for EEG and MEG sensors

../_images/sphx_glr_plot_left_cerebellum_volume_source_thumb.png

Generate a left cerebellum volume source space

../_images/sphx_glr_plot_read_bem_surfaces_thumb.png

Reading BEM surfaces from a forward solution

../_images/sphx_glr_plot_source_space_morphing_thumb.png

Use source space morphing

../_images/sphx_glr_plot_compute_mne_inverse_epochs_in_label_thumb.png

Compute MNE-dSPM inverse solution on single epochs

../_images/sphx_glr_plot_compute_mne_inverse_raw_in_label_thumb.png

Compute sLORETA inverse solution on raw data

../_images/sphx_glr_plot_compute_mne_inverse_volume_thumb.png

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

../_images/sphx_glr_plot_custom_inverse_solver_thumb.png

Source localization with a custom inverse solver

../_images/sphx_glr_plot_dics_beamformer_thumb.png

Compute DICS beamfomer on evoked data

../_images/sphx_glr_plot_dics_source_power_thumb.png

Compute source power using DICS beamfomer

../_images/sphx_glr_plot_gamma_map_inverse_thumb.png

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

../_images/sphx_glr_plot_label_activation_from_stc_thumb.png

Extracting time course from source_estimate object

../_images/sphx_glr_plot_label_from_stc_thumb.png

Generate a functional label from source estimates

../_images/sphx_glr_plot_label_source_activations_thumb.png

Extracting the time series of activations in a label

../_images/sphx_glr_plot_lcmv_beamformer_thumb.png

Compute LCMV beamformer on evoked data

../_images/sphx_glr_plot_lcmv_beamformer_volume_thumb.png

Compute LCMV inverse solution on evoked data in volume source space

../_images/sphx_glr_plot_mixed_norm_inverse_thumb.png

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

../_images/sphx_glr_plot_mixed_source_space_inverse_thumb.png

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

../_images/sphx_glr_plot_mne_crosstalk_function_thumb.png

Compute cross-talk functions (CTFs) for labels for MNE/dSPM/sLORETA

../_images/sphx_glr_plot_mne_point_spread_function_thumb.png

Compute point-spread functions (PSFs) for MNE/dSPM/sLORETA

../_images/sphx_glr_plot_morph_data_thumb.png

Morph source estimates from one subject to another subject

../_images/sphx_glr_plot_rap_music_thumb.png

Compute Rap-Music on evoked data

../_images/sphx_glr_plot_read_inverse_thumb.png

Reading an inverse operator

../_images/sphx_glr_plot_read_source_space_thumb.png

Reading a source space from a forward operator

../_images/sphx_glr_plot_read_stc_thumb.png

Reading an STC file

../_images/sphx_glr_plot_snr_estimate_thumb.png

Plot an estimate of data SNR

../_images/sphx_glr_plot_tf_dics_thumb.png

Time-frequency beamforming using DICS

../_images/sphx_glr_plot_tf_lcmv_thumb.png

Time-frequency beamforming using LCMV

../_images/sphx_glr_plot_time_frequency_mixed_norm_inverse_thumb.png

Compute MxNE with time-frequency sparse prior

../_images/sphx_glr_plot_read_and_write_raw_data_thumb.png

Reading and writing raw files

../_images/sphx_glr_plot_read_epochs_thumb.png

Reading epochs from a raw FIF file

../_images/sphx_glr_plot_read_evoked_thumb.png

Reading and writing an evoked file

../_images/sphx_glr_plot_read_noise_covariance_matrix_thumb.png

Reading/Writing a noise covariance matrix

../_images/sphx_glr_read_events_thumb.png

Reading an event file

../_images/sphx_glr_plot_define_target_events_thumb.png

Define target events based on time lag, plot evoked response

../_images/sphx_glr_plot_eog_artifact_histogram_thumb.png

Show EOG artifact timing

../_images/sphx_glr_plot_find_ecg_artifacts_thumb.png

Find ECG artifacts

../_images/sphx_glr_plot_find_eog_artifacts_thumb.png

Find EOG artifacts

../_images/sphx_glr_plot_interpolate_bad_channels_thumb.png

Interpolate bad channels for MEG/EEG channels

../_images/sphx_glr_plot_rereference_eeg_thumb.png

Re-referencing the EEG signal

../_images/sphx_glr_plot_resample_thumb.png

Resampling data

../_images/sphx_glr_plot_run_ica_thumb.png

Compute ICA components on epochs

../_images/sphx_glr_plot_shift_evoked_thumb.png

Shifting time-scale in evoked data

../_images/sphx_glr_plot_virtual_evoked_thumb.png

Remap MEG channel types

../_images/sphx_glr_plot_xdawn_denoising_thumb.png

XDAWN Denoising

../_images/sphx_glr_plot_compute_rt_average_thumb.png

Compute real-time evoked responses using moving averages

../_images/sphx_glr_plot_compute_rt_decoder_thumb.png

Decoding real-time data

../_images/sphx_glr_rt_feedback_server_thumb.png

Real-time feedback for decoding :: Server Side

../_images/sphx_glr_plot_simulate_evoked_data_thumb.png

Generate simulated evoked data

../_images/sphx_glr_plot_simulate_raw_data_thumb.png

Generate simulated raw data

../_images/sphx_glr_plot_cluster_stats_evoked_thumb.png

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

../_images/sphx_glr_plot_fdr_stats_evoked_thumb.png

FDR correction on T-test on sensor data

../_images/sphx_glr_plot_linear_regression_raw_thumb.png

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

../_images/sphx_glr_plot_sensor_permutation_test_thumb.png

Permutation T-test on sensor data

../_images/sphx_glr_plot_sensor_regression_thumb.png

Sensor space least squares regression

../_images/sphx_glr_plot_compute_raw_data_spectrum_thumb.png

Compute the power spectral density of raw data

../_images/sphx_glr_plot_compute_source_psd_epochs_thumb.png

Compute Power Spectral Density of inverse solution from single epochs

../_images/sphx_glr_plot_source_label_time_frequency_thumb.png

Compute power and phase lock in label of the source space

../_images/sphx_glr_plot_source_power_spectrum_thumb.png

Compute power spectrum densities of the sources with dSPM

../_images/sphx_glr_plot_source_space_time_frequency_thumb.png

Compute induced power in the source space with dSPM

../_images/sphx_glr_plot_temporal_whitening_thumb.png

Temporal whitening with AR model

../_images/sphx_glr_make_report_thumb.png

Make an MNE-Report with a Slider

../_images/sphx_glr_plot_channel_epochs_image_thumb.png

Visualize channel over epochs as an image

../_images/sphx_glr_plot_eeg_on_scalp_thumb.png

Plotting EEG sensors on the scalp

../_images/sphx_glr_plot_evoked_topomap_thumb.png

Plotting topographic maps of evoked data

../_images/sphx_glr_plot_evoked_whitening_thumb.png

Whitening evoked data with a noise covariance

../_images/sphx_glr_plot_meg_sensors_thumb.png

Plotting sensor layouts of MEG systems

../_images/sphx_glr_plot_parcellation_thumb.png

Plot a cortical parcellation

../_images/sphx_glr_plot_sensor_noise_level_thumb.png

Show noise levels from empty room data

../_images/sphx_glr_plot_ssp_projs_sensitivity_map_thumb.png

Sensitivity map of SSP projections

../_images/sphx_glr_plot_topo_compare_conditions_thumb.png

Compare evoked responses for different conditions

../_images/sphx_glr_plot_topo_customized_thumb.png

Plot custom topographies for MEG sensors

../_images/sphx_glr_plot_artifacts_correction_filtering_thumb.png

Filtering and resampling data

../_images/sphx_glr_plot_artifacts_correction_ica_thumb.png

Artifact Correction with ICA

../_images/sphx_glr_plot_artifacts_correction_maxwell_filtering_thumb.png

Artifact correction with Maxwell filter

../_images/sphx_glr_plot_artifacts_correction_rejection_thumb.png

Rejecting bad data (channels and segments)

../_images/sphx_glr_plot_artifacts_correction_ssp_thumb.png

Artifact Correction with SSP

../_images/sphx_glr_plot_artifacts_detection_thumb.png

Introduction to artifacts and artifact detection

../_images/sphx_glr_plot_compute_covariance_thumb.png

Computing covariance matrix

../_images/sphx_glr_plot_configuration_thumb.png

Background information on configurations

../_images/sphx_glr_plot_dipole_fit_thumb.png

Source localization with single dipole fit

../_images/sphx_glr_plot_ecog_thumb.png

Working with ECoG data

../_images/sphx_glr_plot_eeg_erp_thumb.png

EEG processing and Event Related Potentials (ERPs)

../_images/sphx_glr_plot_epoching_and_averaging_thumb.png

Epoching and averaging (ERP/ERF)

../_images/sphx_glr_plot_epochs_to_data_frame_thumb.png

Export epochs to Pandas DataFrame

../_images/sphx_glr_plot_forward_thumb.png

Head model and forward computation

../_images/sphx_glr_plot_ica_from_raw_thumb.png

Compute ICA on MEG data and remove artifacts

../_images/sphx_glr_plot_info_thumb.png

The Info data structure

../_images/sphx_glr_plot_introduction_thumb.png

Basic MEG and EEG data processing

../_images/sphx_glr_plot_mne_dspm_source_localization_thumb.png

Source localization with MNE/dSPM/sLORETA

../_images/sphx_glr_plot_modifying_data_inplace_thumb.png

Modifying data in-place

../_images/sphx_glr_plot_object_epochs_thumb.png

The Epochs data structure: epoched data

../_images/sphx_glr_plot_object_evoked_thumb.png

The Evoked data structure: evoked/averaged data

../_images/sphx_glr_plot_object_raw_thumb.png

The Raw data structure: continuous data

../_images/sphx_glr_plot_point_spread_thumb.png

Corrupt known signal with point spread

../_images/sphx_glr_plot_sensors_decoding_thumb.png

Decoding sensor space data

../_images/sphx_glr_plot_stats_cluster_1samp_test_time_frequency_thumb.png

Non-parametric 1 sample cluster statistic on single trial power

../_images/sphx_glr_plot_stats_cluster_spatio_temporal_thumb.png

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

../_images/sphx_glr_plot_stats_cluster_spatio_temporal_2samp_thumb.png

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

../_images/sphx_glr_plot_stats_cluster_spatio_temporal_repeated_measures_anova_thumb.png

Repeated measures ANOVA on source data with spatio-temporal clustering

../_images/sphx_glr_plot_stats_cluster_time_frequency_thumb.png

Non-parametric between conditions cluster statistic on single trial power

../_images/sphx_glr_plot_stats_cluster_time_frequency_repeated_measures_anova_thumb.png

Mass-univariate twoway repeated measures ANOVA on single trial power

../_images/sphx_glr_plot_stats_spatio_temporal_cluster_sensors_thumb.png

Spatiotemporal permutation F-test on full sensor data

../_images/sphx_glr_plot_visualize_epochs_thumb.png

Visualize Epochs data

../_images/sphx_glr_plot_visualize_evoked_thumb.png

Visualize Evoked data

../_images/sphx_glr_plot_visualize_raw_thumb.png

Visualize Raw data