# Tutorials & Courses

Below are a selected collection of tutorials and courses I have created for applying different signal analysis tools to electrophysiological data.

### [COURSE] An introduction to signal processing in MNE

Resources for an introductory course for signal processing in the MNE-Python and MNE-Connectivity packages.

### [TUTORIAL] MNE-Connectivity: Comparison of coherency-based methods

This example demonstrates the distinct forms of information captured by coherency-based connectivity methods, and highlights the scenarios in which these different methods should be applied.

### [TUTORIAL] MNE-Connectivity: Compute multivariate coherency/coherence

This example demonstrates how canonical coherency (CaCoh) - a multivariate method based on coherency - can be used to compute connectivity between whole sets of sensors, alongside spatial patterns of the connectivity.

### [TUTORIAL] MNE-Connectivity: Compute multivariate measures of the imaginary part of coherency

This example demonstrates how multivariate methods based on the imaginary part of coherency can be used to compute connectivity between whole sets of sensors, and how spatial patterns of this connectivity can be interpreted. The methods in question are: the maximised imaginary part of coherency (MIC); and the multivariate interaction measure (MIM; as well as its extension, the global interaction measure, GIM).

### [TUTORIAL] MNE-Connectivity: Compute directionality of connectivity with multivariate Granger causality

This example demonstrates how Granger causality based on state-space models can be used to compute directed connectivity between sensors in a multivariate manner. Furthermore, the use of time-reversal for improving the robustness of directed connectivity estimates to noise in the data is discussed.

### [TUTORIAL] PyBispectra: Compute phase-amplitude coupling

This example demonstrates how phase-amplitude coupling (PAC) can be computed with PyBispectra.

### [TUTORIAL] PyBispectra: Compute time delay estimates

This example demonstrates how time delay estimation (TDE) can be computed with PyBispectra.

### [TUTORIAL] PyBispectra: Compute time delay estimates for frequency bands

This example demonstrates how time delay estimation (TDE) for different frequency bands can be computed with PyBispectra.

### [TUTORIAL] PyBispectra: Compute waveshape features

This example demonstrates how waveshape features can be computed with PyBispectra.

### [TUTORIAL] PyBispectra: Spatio-spectral filtering for waveshape analysis

This example demonstrates how spatio-spectral filtering can be incorporated into waveshape analysis with PyBispectra.

### [TUTORIAL] PyBispectra: Compute the bispectrum and threenorm

This example demonstrates how the bispectrum and threenorm can be computed with PyBispectra.

### [TUTORIAL] PyPARRM: Using PyPARRM to filter out stimulation artefacts from data

This example demonstrates how the PARRM algorithm can be used to identify and remove stimulation artefacts from electrophysiological data in the PyPARRM package.

### [TUTORIAL] PyPARRM: Real data example: stimulation artefacts in human ECoG data

This example demonstrates the utility of PARRM on a genuine recording of human brain activity from local field potentials (LFPs) at the site of deep brain stimulation in the subthalamic nucleus, and from electrocorticography (ECoG) at the cortex.