This talk covers the differences between applying HED annotation to fMRI datasets versus other neuroimaging practices, and also introduces an analysis pipeline using HED tags. 

JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.

Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.

In this lesson you will learn how to simulate seizure events and epilepsy in The Virtual Brain. We will look at the paper On the Nature of Seizure Dynamics, which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor. In this part, we focus on setting up the parameters.

Longitudinal Online Research and Imaging System (LORIS) is a web-based data and project management software for neuroimaging research studies. It is an open source framework for storing and processing behavioural, clinical, neuroimaging and genetic data. LORIS also makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.

This talk covers the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC), a free one-stop-shop collaboratory for science researchers that need resources such as neuroimaging analysis software, publicly available data sets, or computing power.

This lesson introduces the EEGLAB toolbox, as well as motivations for its use.

In this lesson, you will learn about the biological activity which generates and is measured by the EEG signal.

This lesson goes over the characteristics of EEG signals when analyzed in source space (as opposed to sensor space). 

This lesson describes the development of EEGLAB as well as to what extent it is used by the research community.

This lesson provides instruction as to how to build a processing pipeline in EEGLAB for a single participant. 

Whereas the previous lesson of this course outlined how to build a processing pipeline for a single participant, this lesson discusses analysis pipelines for multiple participants simultaneously. 

In addition to outlining the motivations behind preprocessing EEG data in general, this lesson covers the first step in preprocessing data with EEGLAB, importing raw data. 

Continuing along the EEGLAB preprocessing pipeline, this tutorial walks users through how to import data events as well as EEG channel locations.

This tutorial instructs users how to visually inspect partially pre-processed neuroimaging data in EEGLAB, specifically how to use the data browser to investigate specific channels, epochs, or events for removable artifacts, biological (e.g., eye blinks, muscle movements, heartbeat) or otherwise (e.g., corrupt channel, line noise). 

This tutorial provides instruction on how to use EEGLAB to further preprocess EEG datasets by identifying and discarding bad channels which, if left unaddressed, can corrupt and confound subsequent analysis steps. 

Users following this tutorial will learn how to identify and discard bad EEG data segments using the MATLAB toolbox EEGLAB. 

This demonstration walks through how to import your data into MATLAB.

This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses. 

This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.