Introductory tutorials to MNE.
Introduction to Python
Configuring MNE python
Introduction to artifacts and artifact detection
The Evoked data structure: evoked/averaged data
Visualize Raw data
Visualize Epochs data
Artifact correction with Maxwell filter
Plotting whitened data
The Info data structure
Modifying data in-place
Working with ECoG data
Artifact Correction with SSP
Filtering and resampling data
Computing a covariance matrix
4D Neuroimaging/BTi phantom dataset tutorial
Source alignment and coordinate frames
Epoching and averaging (ERP/ERF)
Rejecting bad data (channels and segments)
The Raw data structure: continuous data
The Epochs data structure: epoched data
Brainstorm CTF phantom dataset tutorial
Computing various MNE solutions
Creating MNE’s data structures from scratch
Frequency and time-frequency sensors analysis
Head model and forward computation
Source localization with equivalent current dipole (ECD) fit
Visualize Evoked data
Non-parametric 1 sample cluster statistic on single trial power
Brainstorm Elekta phantom dataset tutorial
Compute ICA on MEG data and remove artifacts
Pandas querying and metadata with Epochs objects
Non-parametric between conditions cluster statistic on single trial power
2 samples permutation test on source data with spatio-temporal clustering
EEG processing and Event Related Potentials (ERPs)
Corrupt known signal with point spread
Visualising statistical significance thresholds on EEG data
Decoding sensor space data (MVPA)
Source localization with MNE/dSPM/sLORETA/eLORETA
Basic MEG and EEG data processing
The role of dipole orientations in distributed source localization
Artifact Correction with ICA
Spatiotemporal permutation F-test on full sensor data
Export epochs to Pandas DataFrame
Permutation t-test on source data with spatio-temporal clustering
Mass-univariate twoway repeated measures ANOVA on single trial power
Repeated measures ANOVA on source data with spatio-temporal clustering
Statistical inference
Brainstorm auditory tutorial dataset
DICS for power mapping
Spectro-temporal receptive field (STRF) estimation on continuous data
Background information on filtering
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