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added script to parse categories and show them just like tags
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| - name: Example MATLAB scripts | ||
| link: /example | ||
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| - name: Apply non-parametric statistics with clustering on TFRs of power that were computed with BESA | ||
| link: /example/apply_clusterrandanalysis_on_tfrs_of_power_that_were_computed_with_besa | ||
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| - name: BIDS - the brain imaging data structure | ||
| link: /example/bids | ||
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| - name: Converting an example audio dataset for sharing in BIDS | ||
| link: /example/bids_audio | ||
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| - name: Converting an example behavioral dataset for sharing in BIDS | ||
| link: /example/bids_behavioral | ||
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| - name: Converting an example EEG dataset for sharing in BIDS | ||
| link: /example/bids_eeg | ||
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| - name: Converting an example EMG dataset for sharing in BIDS | ||
| link: /example/bids_emg | ||
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| - name: Converting an example eyetracker dataset for sharing in BIDS | ||
| link: /example/bids_eyetracker | ||
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| - name: Converting an example MEG dataset for sharing in BIDS | ||
| link: /example/bids_meg | ||
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| - name: Converting an example motion tracking dataset for sharing in BIDS | ||
| link: /example/bids_motion | ||
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| - name: Converting the combined MEG/fMRI MOUS dataset for sharing in BIDS | ||
| link: /example/bids_mous | ||
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| - name: Converting an example NIRS dataset for sharing in BIDS | ||
| link: /example/bids_nirs | ||
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| - name: Combining simultaneous recordings in BIDS | ||
| link: /example/bids_pom | ||
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| - name: Converting an example video dataset for sharing in BIDS | ||
| link: /example/bids_video | ||
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| - name: Example analysis pipeline for Biosemi data | ||
| link: /example/biosemi | ||
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| - name: How to use ft_checkconfig | ||
| link: /example/checkconfig | ||
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| - name: Effect of SNR on Coherence | ||
| link: /example/coherence_snr | ||
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| - name: Combined EEG and MEG source reconstruction | ||
| link: /example/combined_eeg_and_meg_source_reconstruction | ||
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| - name: The correct pipeline order for combining planar MEG channels | ||
| link: /example/combineplanar_pipelineorder | ||
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| - name: Common filters in beamforming | ||
| link: /example/common_filters_in_beamforming | ||
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| - name: Compute forward simulated data using ft_dipolesimulation | ||
| link: /example/compute_forward_simulated_data | ||
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| - name: Compute forward simulated data and apply a beamformer scan | ||
| link: /example/compute_forward_simulated_data_and_apply_a_beamformer_scan | ||
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| - name: Compute forward simulated data and apply a dipole fit | ||
| link: /example/compute_forward_simulated_data_and_apply_a_dipole_fit | ||
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| - name: Compute forward simulated data with the low-level ft_compute_leadfield | ||
| link: /example/compute_leadfield | ||
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| - name: Conditional Granger causality in the frequency domain | ||
| link: /example/connectivity_conditional_granger | ||
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| - name: Check the quality of the anatomical coregistration | ||
| link: /example/coregistration_quality_control | ||
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| - name: Correlation analysis of fMRI data | ||
| link: /example/correlation_analysis_in_fmri_data | ||
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| - name: Cross-frequency analysis | ||
| link: /example/crossfreq | ||
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| - name: Detect the muscle activity in an EMG channel and use that as trial definition | ||
| link: /example/detect_the_muscle_activity_in_an_emg_channel_and_use_that_as_trial_definition | ||
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| - name: Determine the filter characteristics | ||
| link: /example/determine_the_filter_characteristics | ||
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| - name: Localizing the sources underlying the difference in event-related fields | ||
| link: /example/difference_erf | ||
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| - name: Fit a dipole to the tactile ERF after mechanical stimulation | ||
| link: /example/dipolefit_somatosensory_erf | ||
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| - name: Analysis of high-gamma band signals in human ECoG | ||
| link: /example/ecog_ny | ||
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| - name: Effects of tapering for power estimates | ||
| link: /example/effects_of_tapering | ||
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| - name: Computing and reporting the effect size | ||
| link: /example/effectsize | ||
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| - name: Align EEG electrode positions to BEM headmodel | ||
| link: /example/electrodes2bem | ||
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| - name: Perform modified Multiscale Entropy (mMSE) analysis on EEG/MEG/LFP data | ||
| link: /example/entropy_analysis | ||
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| - name: Compute EEG leadfields using a FEM headmodel | ||
| link: /example/fem | ||
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| - name: How to create a head model if you do not have an individual MRI | ||
| link: /example/fittemplate | ||
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| - name: Fixing a missing channel | ||
| link: /example/fixing_a_missing_sensor | ||
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| - name: Fitting oscillations and one-over-F (FOOOF) | ||
| link: /example/fooof | ||
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| - name: Example real-time average | ||
| link: /example/ft_realtime_average | ||
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| - name: Example real-time classification | ||
| link: /example/ft_realtime_classification | ||
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| - name: Realtime neurofeedback application based on Hilbert phase estimation | ||
| link: /example/ft_realtime_hilbert | ||
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| - name: Example real-time power estimate | ||
| link: /example/ft_realtime_powerestimate | ||
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| - name: Example real-time selective average | ||
| link: /example/ft_realtime_selectiveaverage | ||
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| - name: Example real-time signal viewer | ||
| link: /example/ft_realtime_signalviewer | ||
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| - name: Getting started with reading raw EEG or MEG data | ||
| link: /example/getting_started_with_reading_raw_eeg_or_meg_data | ||
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| - name: Using General Linear Modeling on time series data | ||
| link: /example/glm_timeseries | ||
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| - name: Using General Linear Modeling over trials | ||
| link: /example/glm_trials | ||
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| - name: How to incorporate head movements in MEG analysis | ||
| link: /example/headmovement_meg | ||
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| - name: Independent component analysis (ICA) to remove ECG artifacts | ||
| link: /example/ica_ecg | ||
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| - name: Independent component analysis (ICA) to remove EOG artifacts | ||
| link: /example/ica_eog | ||
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| - name: How to import data from MNE-Python and FreeSurfer | ||
| link: /example/import_mne | ||
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| - name: Irregular Resampling Auto-Spectral Analysis (IRASA) | ||
| link: /example/irasa | ||
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| - name: Make MEG leadfields using different headmodels | ||
| link: /example/make_leadfields_using_different_headmodels | ||
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| - name: Measuring the timing delay and jitter for a real-time application | ||
| link: /example/measuring_the_timing_delay_and_jitter_for_a_real-time_application | ||
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| - name: Combine MEG with Eyelink eyetracker data | ||
| link: /example/meg_eyelink | ||
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| - name: Interpolating data from the CTF151 to the CTF275 sensor array using megrealign | ||
| link: /example/megrealign | ||
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| - name: Defining electrodes as neighbours for cluster-level statistics | ||
| link: /example/neighbours | ||
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| - name: Analyzing NIRS data recorded during unilateral finger- and foot-tapping | ||
| link: /example/nirs_fingertapping | ||
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| - name: Using GLM to analyze NIRS timeseries data | ||
| link: /example/nirs_glm | ||
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| - name: Creating a layout for plotting NIRS optodes and channels | ||
| link: /example/nirs_layout | ||
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| - name: Analyzing NIRS data recorded during listening to and repeating speech | ||
| link: /example/nirs_speech | ||
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| - name: Simulate an oscillatory signal with phase resetting | ||
| link: /example/phase_reset | ||
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| - name: Find the orientation of planar gradiometers | ||
| link: /example/planar_orientation | ||
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| - name: Plotting the result of source reconstruction on a cortical mesh | ||
| link: /example/plotting_source_surface | ||
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| - name: Making your analysis pipeline reproducible using reproducescript | ||
| link: /example/reproducescript | ||
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| - name: Using reproducescript on a full study | ||
| link: /example/reproducescript_andersen | ||
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| - name: Using reproducescript for a group analysis | ||
| link: /example/reproducescript_group | ||
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| - name: Re-reference EEG and iEEG data | ||
| link: /example/rereference | ||
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| - name: Using simulations to estimate the sample size for cluster-based permutation test | ||
| link: /example/samplesize | ||
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| - name: Source statistics | ||
| link: /example/source_statistics | ||
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| - name: Create MNI-aligned grids in individual head-space | ||
| link: /example/sourcemodel_aligned2mni | ||
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| - name: Fitting a template MRI to the MEG Polhemus head shape | ||
| link: /example/sphere_fitting | ||
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| - name: Analyze Steady-State Visual Evoked Potentials (SSVEPs) | ||
| link: /example/ssvep | ||
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| - name: Stratify the distribution of one variable that differs in two conditions | ||
| link: /example/stratify | ||
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| - name: Symmetric dipole pairs for beamforming | ||
| link: /example/symmetry | ||
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| - name: Testing BEM created EEG lead fields | ||
| link: /example/testing_bem_created_leadfields | ||
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| - name: Using threshold-free cluster enhancement for cluster statistics | ||
| link: /example/threshold_free_cluster_enhancement | ||
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| - name: Making your own trialfun for conditional trial definition | ||
| link: /example/trialfun | ||
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| - name: Use denoising source separation (DSS) to remove ECG artifacts | ||
| link: /example/use_denoising_source_separation_dss_to_remove_ecg_artifacts | ||
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| - name: Use simulated ERPs to explore cluster statistics | ||
| link: /example/use_simulated_erps_to_explore_cluster_statistics | ||
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| - name: Use your own forward leadfield model in an inverse beamformer computation | ||
| link: /example/use_your_own_forward_leadfield_model_in_an_inverse_beamformer_computation | ||
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| - name: Making a synchronous movie of EEG or NIRS combined with video recordings | ||
| link: /example/video_eeg | ||
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| - name: Can I create an artificial CTF dataset using MATLAB? | ||
| link: /example/writing_simulated_data_to_a_ctf_dataset | ||
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