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This tutorial covers the fundamentals of collaborating with Git and GitHub.

Difficulty level: Intermediate
Duration: 2:15:50
Speaker: : Elizabeth DuPre

This lecture describes the principles of EEG electrode placement in both 2- and 3-dimensional formats. 

Difficulty level: Intermediate
Duration: 12:16
Speaker: : Mike X. Cohen

This tutorial walks users through performing Fourier Transform (FFT) spectral analysis of a single EEG channel using MATLAB. 

Difficulty level: Intermediate
Duration: 13:39
Speaker: : Mike X. Cohen

This tutorial builds on the previous lesson's demonstration of spectral analysis of one EEG channel. Here, users will learn how to compute and visualize spectral power from all EEG channels using MATLAB. 

Difficulty level: Intermediate
Duration: 12:34
Speaker: : Mike X. Cohen

In this lesson, users will learn more about the steady-state visually evoked potential (SSEVP), as well as how to create and interpret topographical maps derived from such studies. 

Difficulty level: Intermediate
Duration: 9:10
Speaker: : Mike X. Cohen

This lesson teaches users how to extract edogenous brain waves from EEG data, specifically oscillations constrained to the 8-12 Hz frequency band, conventionally named alpha. 

Difficulty level: Intermediate
Duration: 13:23
Speaker: : Mike X. Cohen

In the final lesson of this module, users will learn how to correlate endogenous alpha power with SSVEP amplitude from EEG data using MATLAB.

Difficulty level: Intermediate
Duration: 12:36
Speaker: : Mike X. Cohen

This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.

Difficulty level: Intermediate
Duration: 1:30:41
Speaker: : Frank Mazza

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity. 

Difficulty level: Intermediate
Duration: 1:16:10
Speaker: : John Griffiths

This lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.  

Difficulty level: Intermediate
Duration: 1:40:52
Speaker: : Paul Triebkorn

This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package. 

Difficulty level: Intermediate
Duration: 2:22:28
Speaker: : Jake Vanderplas

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

 

This lesson corresponds to slides 65-90 of the PDF below. 

Difficulty level: Intermediate
Duration: 1:15:04
Speaker: : Daniel Hauke

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices. 

Difficulty level: Intermediate
Duration: 1:39:04