From bd187c4b86a716d39e27e03a4cd29466563076e4 Mon Sep 17 00:00:00 2001 From: Robert Oostenveld Date: Mon, 9 Dec 2024 14:47:49 +0100 Subject: [PATCH] updated categories --- _data/category/example.yml | 108 ++++++++++++++++++------------------- 1 file changed, 54 insertions(+), 54 deletions(-) diff --git a/_data/category/example.yml b/_data/category/example.yml index f0802f3a4..6929eaa6c 100644 --- a/_data/category/example.yml +++ b/_data/category/example.yml @@ -1,8 +1,14 @@ - name: Example MATLAB scripts link: /example -- 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 +- name: Common filters in beamforming + link: /example/beamformer_commonfilter + +- name: Use your own forward leadfield model in an inverse beamformer computation + link: /example/beamformer_ownforward + +- name: Testing BEM created EEG lead fields + link: /example/bem_evaluation - name: BIDS - the brain imaging data structure link: /example/bids @@ -49,51 +55,27 @@ - name: Effect of SNR on Coherence link: /example/coherence_snr -- name: Combined EEG and MEG source reconstruction - link: /example/combined_eeg_and_meg_source_reconstruction - - name: The correct pipeline order for combining planar MEG channels link: /example/combineplanar_pipelineorder -- name: Common filters in beamforming - link: /example/common_filters_in_beamforming - -- name: Compute forward simulated data using ft_dipolesimulation - link: /example/compute_forward_simulated_data - -- name: Compute forward simulated data and apply a beamformer scan - link: /example/compute_forward_simulated_data_and_apply_a_beamformer_scan - -- name: Compute forward simulated data and apply a dipole fit - link: /example/compute_forward_simulated_data_and_apply_a_dipole_fit - - name: Compute forward simulated data with the low-level ft_compute_leadfield link: /example/compute_leadfield -- name: Conditional Granger causality in the frequency domain - link: /example/connectivity_conditional_granger - - name: Check the quality of the anatomical coregistration link: /example/coregistration_quality_control -- name: Correlation analysis of fMRI data - link: /example/correlation_analysis_in_fmri_data - - name: Cross-frequency analysis link: /example/crossfreq -- 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 - -- name: Determine the filter characteristics - link: /example/determine_the_filter_characteristics - - name: Localizing the sources underlying the difference in event-related fields link: /example/difference_erf - name: Fit a dipole to the tactile ERF after mechanical stimulation link: /example/dipolefit_somatosensory_erf +- name: Use denoising source separation (DSS) to remove ECG artifacts + link: /example/dss_ecg + - name: Analysis of high-gamma band signals in human ECoG link: /example/ecog_ny @@ -112,12 +94,18 @@ - name: Compute EEG leadfields using a FEM headmodel link: /example/fem +- name: Determine the filter characteristics + link: /example/filter_characteristics + - name: How to create a head model if you do not have an individual MRI link: /example/fittemplate - name: Fixing a missing channel link: /example/fixing_a_missing_sensor +- name: Correlation analysis of fMRI data + link: /example/fmri_correlationanalysis + - name: Fitting oscillations and one-over-F (FOOOF) link: /example/fooof @@ -139,15 +127,18 @@ - name: Example real-time signal viewer link: /example/ft_realtime_signalviewer -- name: Getting started with reading raw EEG or MEG data - link: /example/getting_started_with_reading_raw_eeg_or_meg_data - - name: Using General Linear Modeling on time series data link: /example/glm_timeseries - name: Using General Linear Modeling over trials link: /example/glm_trials +- name: Conditional Granger causality in the frequency domain + link: /example/granger_conditional + +- name: Make MEG leadfields using different headmodels + link: /example/headmodel_various + - name: How to incorporate head movements in MEG analysis link: /example/headmovement_meg @@ -163,12 +154,6 @@ - name: Irregular Resampling Auto-Spectral Analysis (IRASA) link: /example/irasa -- name: Make MEG leadfields using different headmodels - link: /example/make_leadfields_using_different_headmodels - -- 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 - - name: Combine MEG with Eyelink eyetracker data link: /example/meg_eyelink @@ -199,6 +184,12 @@ - name: Plotting the result of source reconstruction on a cortical mesh link: /example/plotting_source_surface +- name: Getting started with reading raw EEG or MEG data + link: /example/raw_meeg + +- name: Measuring the timing delay and jitter for a real-time application + link: /example/realtime_evaluation + - name: Making your analysis pipeline reproducible using reproducescript link: /example/reproducescript @@ -214,45 +205,54 @@ - name: Using simulations to estimate the sample size for cluster-based permutation test link: /example/samplesize +- name: Compute forward simulated data using ft_dipolesimulation + link: /example/simulateddata + +- name: Compute forward simulated data and apply a beamformer scan + link: /example/simulateddata_beamformer + +- name: Use simulated ERPs to explore cluster statistics + link: /example/simulateddata_clusterstats + +- name: Can I create an artificial CTF dataset using MATLAB? + link: /example/simulateddata_ctf + +- name: Compute forward simulated data and apply a dipole fit + link: /example/simulateddata_dipolefit + - name: Source statistics link: /example/source_statistics - name: Create MNI-aligned grids in individual head-space link: /example/sourcemodel_aligned2mni +- name: Combined EEG and MEG source reconstruction + link: /example/sourcerecon_meeg + - name: Fitting a template MRI to the MEG Polhemus head shape link: /example/sphere_fitting - name: Analyze Steady-State Visual Evoked Potentials (SSVEPs) link: /example/ssvep +- name: Apply non-parametric statistics with clustering on TFRs of power that were computed with BESA + link: /example/stats_besa + - name: Stratify the distribution of one variable that differs in two conditions link: /example/stratify - name: Symmetric dipole pairs for beamforming link: /example/symmetry -- name: Testing BEM created EEG lead fields - link: /example/testing_bem_created_leadfields - - name: Using threshold-free cluster enhancement for cluster statistics - link: /example/threshold_free_cluster_enhancement + link: /example/tfce + +- name: Detect the muscle activity in an EMG channel and use that as trial definition + link: /example/trialdef_emg - name: Making your own trialfun for conditional trial definition link: /example/trialfun -- name: Use denoising source separation (DSS) to remove ECG artifacts - link: /example/use_denoising_source_separation_dss_to_remove_ecg_artifacts - -- name: Use simulated ERPs to explore cluster statistics - link: /example/use_simulated_erps_to_explore_cluster_statistics - -- 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 - - name: Making a synchronous movie of EEG or NIRS combined with video recordings link: /example/video_eeg -- name: Can I create an artificial CTF dataset using MATLAB? - link: /example/writing_simulated_data_to_a_ctf_dataset -