Course:

In this lesson, users will learn about human brain signals as measured by electroencephalography (EEG), as well as associated neural signatures such as steady state visually evoked potentials (SSVEPs) and alpha oscillations.

Difficulty level: Intermediate

Duration: 8:51

Speaker: : Mike X. Cohen

Course:

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

Course:

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

Course:

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

Course:

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

Course:

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

Course:

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 a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

Difficulty level: Intermediate

Duration: 1:11:04

Speaker: : Etay Hay

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

Course:

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 on generating TVB ready imaging data by Paul Triebkorn is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Difficulty level: Intermediate

Duration: 1:40:52

Speaker: : Paul Triebkorn

Course:

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.

Difficulty level: Intermediate

Duration: 1:47:22

Speaker: : Erin Dickie and John Griffiths

Course:

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

Speaker: : Erin Dickie and John Griffiths

Course:

This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate

Duration: 50:44

Speaker: : Caterina Gratton

Course:

This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and go through both motivation and process involved in moving your research computing to the cloud. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate

Duration: 3:09:12

Speaker: : Amanda Tan

This lecture 1/15 is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 0:40

Speaker: : Florence I. Kleberg

This lecture (2/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:23

Speaker: : Florence I. Kleberg

This lecture (3/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:20

Speaker: : Florence I. Kleberg

This lecture (4/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:08

Speaker: : Florence I. Kleberg

This lecture (5/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate

Duration: 1:18

Speaker: : Florence I. Kleberg

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