![]() ![]() Source localization with a custom inverse solverĬompute source power using DICS beamformerĬompute evoked ERS source power using DICS, LCMV beamformer, and dSPMĬompute a sparse inverse solution using the Gamma-MAP empirical Bayesian methodĮxtracting time course from source_estimate object Generate a left cerebellum volume source spaceĬompute MNE-dSPM inverse solution on single epochsĬompute sLORETA inverse solution on raw dataĬompute MNE-dSPM inverse solution on evoked data in volume source space ![]() Receptive Field Estimation and PredictionĬompute Spectro-Spatial Decomposition (SSD) spatial filtersĭisplay sensitivity maps for EEG and MEG sensors Linear classifier on sensor data with plot patterns and filters Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)ĭecoding in time-frequency space using Common Spatial Patterns (CSP)ĭecoding sensor space data with generalization across time and conditionsĪnalysis of evoked response using ICA and PCA reduction techniquesĬompute effect-matched-spatial filtering (EMS) Machine Learning (Decoding, Encoding, and MVPA) Permutation F-test on sensor data with 1D cluster levelĪnalysing continuous features with binning and regression in sensor space Time-frequency on simulated data (Multitaper vs. Plot single trial activity, grouped by ROI and sorted by RTĬompare evoked responses for different conditionsĬompute a cross-spectral density (CSD) matrixĬompute Power Spectral Density of inverse solution from single epochsĬompute power and phase lock in label of the source spaceĬompute source power spectral density (PSD) in a labelĬompute source power spectral density (PSD) of VectorView and OPM dataĬompute induced power in the source space with dSPMĮxplore event-related dynamics for specific frequency bands Whitening evoked data with a noise covariance Plotting topographic arrowmaps of evoked data Visualize channel over epochs as an image How to convert 3D electrode positions to a 2D image Plot sensor denoising using oversampled temporal projection Maxwell filter data with movement compensationĪnnotate movement artifacts and reestimate dev_head_t Interpolate bad channels for MEG/EEG channels Visualise NIRS artifact correction methodsĬompare the different ICA algorithms in MNE Transform EEG data using current source density (CSD) Identify EEG Electrodes Bridged by too much Gel Reading/Writing a noise covariance matrixĬortical Signal Suppression (CSS) for removal of cortical signalsĭefine target events based on time lag, plot evoked response ![]() How to use data in neural ensemble (NEO) format Sleep stage classification from polysomnography (PSG) dataĬreating MNE-Python data structures from scratch Spectro-temporal receptive field (STRF) estimation on continuous data Machine learning models of neural activity Repeated measures ANOVA on source data with spatio-temporal clustering Permutation t-test on source data with spatio-temporal clusteringĢ samples permutation test on source data with spatio-temporal clustering Spatiotemporal permutation F-test on full sensor data Mass-univariate twoway repeated measures ANOVA on single trial power Non-parametric between conditions cluster statistic on single trial power Non-parametric 1 sample cluster statistic on single trial power Visualising statistical significance thresholds on EEG data Source reconstruction using an LCMV beamformerĮEG source localization given electrode locations on an MRIīrainstorm Elekta phantom dataset tutorialĤD Neuroimaging/BTi phantom dataset tutorial The role of dipole orientations in distributed source localization Source localization with MNE, dSPM, sLORETA, and eLORETA Source localization with equivalent current dipole (ECD) fit Using an automated approach to coregistration The Evoked data structure: evoked/averaged dataĮEG analysis - Event-Related Potentials (ERPs)įrequency and time-frequency sensor analysisįrequency-tagging: Basic analysis of an SSVEP/vSSR dataset The Epochs data structure: discontinuous dataĭivide continuous data into equally-spaced epochs Preprocessing functional near-infrared spectroscopy (fNIRS) data Signal-space separation (SSS) and Maxwell filtering Working with CTF data: the Brainstorm auditory datasetīuilt-in plotting methods for Raw objectsĮxtracting and visualizing subject head movement Reading data for different recording systems ![]() Overview of MEG/EEG analysis with MNE-Python ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |