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In this tutorial, users learn about the various filtering options in EEGLAB, how to inspect channel properties for noisy signals, as well as how to filter out specific components of EEG data (e.g., electrical line noise).

Difficulty level: Beginner
Duration: 10:46
Speaker: : Arnaud Delorme

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). 

Difficulty level: Beginner
Duration: 5:08
Speaker: : Arnaud Delorme

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. 

Difficulty level: Beginner
Duration: 13:01
Speaker: : Arnaud Delorme

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

Difficulty level: Beginner
Duration: 11:25
Speaker: : Arnaud Delorme

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 lecture contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.

Difficulty level: Beginner
Duration: 18:56
Speaker: : Tom Johnstone

To explore the challenges and the ethical issues raised by advances in do-it-yourself (DIY) neurotechnology, the Emerging Issues Task Force of the International Neuroethics Society organized a virtual panel discussion. The panel discussed neurotechnologies such as transcranial direct current stimulation (tDCS) and electroencephalogram (EEG) headsets and their ability to change the way we understand and alter our brains. Particular attention will be given to the use of neurotechnology by everyday people and the implications this has for regulatory oversight and citizen neuroscience. 

Difficulty level: Beginner
Duration: 1:00:59

This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more. 

Difficulty level: Beginner
Duration: 13:36
Speaker: : Harrison Canning
Course:

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.

 

Difficulty level: Beginner
Duration: 1:09:16
Speaker: : Aaron J. Newman

Hierarchical Event Descriptors (HED) fill a major gap in the neuroinformatics standards toolkit, namely the specification of the nature(s) of events and time-limited conditions recorded as having occurred during time series recordings (EEG, MEG, iEEG, fMRI, etc.). Here, the HED Working Group presents an online INCF workshop on the need for, structure of, tools for, and use of HED annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. 

     

    Difficulty level: Beginner
    Duration: 03:37:42
    Speaker: :

    This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

    Difficulty level: Intermediate
    Duration: 1:09:33
    Speaker: : Sean Hill

    This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

    This lesson corresponds to slides 1-64 in the PDF below. 

    Difficulty level: Intermediate
    Duration: 1:28:14

    This talk gives a brief overview of current efforts to collect and share the Brain Reference Architecture (BRA) data involved in the construction of a whole-brain architecture that assigns functions to major brain organs. 

    Difficulty level: Beginner
    Duration: 4:02

    This brief talk discusses the idea that music, as a naturalistic stimulus, offers a window into higher cognition and various levels of neural architecture.    

    Difficulty level: Beginner
    Duration: 4:04
    Speaker: : Sarah Faber

    In this short talk you will learn about The Neural System Laboratory, which aims to develop and implement new technologies for analysis of brain architecture, connectivity, and brain-wide gene and molecular level organization.

    Difficulty level: Beginner
    Duration: 8:38
    Speaker: : Trygve Leergard

    Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks. 

    Difficulty level: Intermediate
    Duration: 6:00
    Speaker: : Marcus Ghosh

    This lesson goes over some examples of how machine learners and computational neuroscientists go about designing and building neural network models inspired by biological brain systems. 

    Difficulty level: Intermediate
    Duration: 12:52
    Speaker: : Dan Goodman

    This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

    Difficulty level: Intermediate
    Duration: 50:44
    Speaker: : Caterina Gratton

    This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

    Difficulty level: Advanced
    Duration: 50:28
    Speaker: : Pierre Bellec
    Course:

    Neuronify is an educational tool meant to create intuition for how neurons and neural networks behave. You can use it to combine neurons with different connections, just like the ones we have in our brain, and explore how changes on single cells lead to behavioral changes in important networks. Neuronify is based on an integrate-and-fire model of neurons. This is one of the simplest models of neurons that exist. It focuses on the spike timing of a neuron and ignores the details of the action potential dynamics. These neurons are modeled as simple RC circuits. When the membrane potential is above a certain threshold, a spike is generated and the voltage is reset to its resting potential. This spike then signals other neurons through its synapses.

    Neuronify aims to provide a low entry point to simulation-based neuroscience.

    Difficulty level: Beginner
    Duration: 01:25
    Speaker: : Neuronify