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    • Examples Gallery
      • Input/Output
      • Data Simulation
      • Preprocessing
      • Visualization
      • Time-Frequency Examples
      • Statistics Examples
      • Machine Learning (Decoding, Encoding, and MVPA)
      • Connectivity Analysis Examples
      • Forward modeling
      • Inverse problem and source analysis
      • Examples on open datasets

    Examples Gallery¶

    Contents

    • Input/Output

    • Data Simulation

    • Preprocessing

    • Visualization

    • Time-Frequency Examples

    • Statistics Examples

    • Machine Learning (Decoding, Encoding, and MVPA)

    • Connectivity Analysis Examples

    • Forward modeling

    • Inverse problem and source analysis

    • Examples on open datasets

    Input/Output¶

    Reading and writing files. See also our Documentation on manipulating data structures.

    ../_images/sphx_glr_plot_elekta_epochs_thumb.png

    Getting averaging info from .fif files¶

    ../_images/sphx_glr_plot_objects_from_arrays_thumb.png

    Creating MNE objects from data arrays¶

    ../_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_events_thumb.png

    Reading an event 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_plot_read_proj_thumb.png

    Read and visualize projections (SSP and other)¶

    Data Simulation¶

    Tools to generate simulation data.

    ../_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_simulated_raw_data_using_subject_anatomy_thumb.png

    Simulate raw data using subject anatomy¶

    ../_images/sphx_glr_plot_source_simulator_thumb.png

    Generate simulated source data¶

    Preprocessing¶

    Examples related to data preprocessing (artifact detection / rejection etc.)

    ../_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_head_positions_thumb.png

    Visualize subject head movement¶

    ../_images/sphx_glr_plot_ica_comparison_thumb.png

    Compare the different ICA algorithms in MNE¶

    ../_images/sphx_glr_plot_interpolate_bad_channels_thumb.png

    Interpolate bad channels for MEG/EEG channels¶

    ../_images/sphx_glr_plot_metadata_query_thumb.png

    Querying epochs with rich metadata¶

    ../_images/sphx_glr_plot_movement_compensation_thumb.png

    Maxwell filter data with movement compensation¶

    ../_images/sphx_glr_plot_otp_thumb.png

    Plot sensor denoising using oversampled temporal projection¶

    ../_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¶

    Visualization¶

    Looking at data and processing output.

    ../_images/sphx_glr_make_report_thumb.png

    Make an MNE-Report with a Slider¶

    ../_images/sphx_glr_plot_3d_to_2d_thumb.png

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

    ../_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_arrowmap_thumb.png

    Plotting topographic arrowmaps of evoked data¶

    ../_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_montage_thumb.png

    Plotting sensor layouts of EEG Systems¶

    ../_images/sphx_glr_plot_parcellation_thumb.png

    Plot a cortical parcellation¶

    ../_images/sphx_glr_plot_roi_erpimage_by_rt_thumb.png

    Plot single trial activity, grouped by ROI and sorted by RT¶

    ../_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_xhemi_thumb.png

    Cross-hemisphere comparison¶

    Time-Frequency Examples¶

    Some examples of how to explore time-frequency content of M/EEG data with MNE.

    ../_images/sphx_glr_plot_compute_csd_thumb.png

    Compute a cross-spectral density (CSD) matrix¶

    ../_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_plot_time_frequency_erds_thumb.png

    Compute and visualize ERDS maps¶

    ../_images/sphx_glr_plot_time_frequency_global_field_power_thumb.png

    Explore event-related dynamics for specific frequency bands¶

    ../_images/sphx_glr_plot_time_frequency_simulated_thumb.png

    Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)¶

    Statistics Examples¶

    Some examples of how to compute statistics on M/EEG data with MNE.

    ../_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

    Analysing continuous features with binning and regression in sensor space¶

    Machine Learning (Decoding, Encoding, and MVPA)¶

    Decoding, encoding, and general machine learning examples.

    ../_images/sphx_glr_decoding_rsa_thumb.png

    Representational Similarity Analysis¶

    ../_images/sphx_glr_plot_decoding_csp_eeg_thumb.png

    Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)¶

    ../_images/sphx_glr_plot_decoding_csp_timefreq_thumb.png

    Decoding in time-frequency 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_spoc_CMC_thumb.png

    Continuous Target Decoding with SPoC¶

    ../_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_receptive_field_mtrf_thumb.png

    Receptive Field Estimation and Prediction¶

    Connectivity Analysis Examples¶

    Examples demonstrating connectivity analysis in sensor and source space.

    ../_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_connectivity_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_envelope_correlation_thumb.png

    Compute envelope correlations in source space¶

    ../_images/sphx_glr_plot_mne_inverse_envelope_correlation_volume_thumb.png

    Compute envelope correlations in volume source space¶

    ../_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¶

    Forward modeling¶

    From BEM segmentation, coregistration, setting up source spaces to actual computation of forward solution.

    ../_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_source_space_morphing_thumb.png

    Use source space morphing¶

    Inverse problem and source analysis¶

    Estimate source activations, extract activations in labels, morph data between subjects etc.

    ../_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_covariance_whitening_dspm_thumb.png

    Demonstrate impact of whitening on source estimates¶

    ../_images/sphx_glr_plot_custom_inverse_solver_thumb.png

    Source localization with a custom inverse solver¶

    ../_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 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_surface_stc_thumb.png

    Morph surface source estimate¶

    ../_images/sphx_glr_plot_morph_volume_stc_thumb.png

    Morph volumetric source estimate¶

    ../_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_vector_mne_solution_thumb.png

    Plotting the full MNE solution¶

    Examples on open datasets¶

    Some demos on common/public datasets using MNE.

    ../_images/sphx_glr_plot_brainstorm_data_thumb.png

    Brainstorm raw (median nerve) dataset¶

    ../_images/sphx_glr_plot_hf_sef_data_thumb.png

    HF-SEF dataset¶

    ../_images/sphx_glr_plot_opm_data_thumb.png

    Optically pumped magnetometer (OPM) data¶

    ../_images/sphx_glr_plot_opm_rest_data_thumb.png

    VectorView and OPM resting state datasets¶

    ../_images/sphx_glr_spm_faces_dataset_thumb.png

    From raw data to dSPM on SPM Faces dataset¶

    Download all examples in Python source code: auto_examples_python.zip

    Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

    Gallery generated by Sphinx-Gallery

    Massachusetts General Hospital Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Massachusetts Institute of Technology New York University Commissariat à l´énergie atomique et aux énergies alternatives Aalto-yliopiston perustieteiden korkeakoulu Télécom ParisTech University of Washington Institut du Cerveau et de la Moelle épinière Boston University Institut national de la santé et de la recherche médicale Forschungszentrum Jülich Technische Universität Ilmenau Berkeley Institute for Data Science Institut national de recherche en informatique et en automatique Aarhus Universitet Karl-Franzens-Universität Graz
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    © Copyright 2012-2019, MNE Developers. Last updated on 2019-07-11.