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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. 

Difficulty level: Beginner
Duration: 8:30
Speaker: : Arnaud Delorme

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

Difficulty level: Beginner
Duration: 11:53
Speaker: : Arnaud Delorme

This tutorial demonstrates how to re-reference and resample raw data in EEGLAB, why such steps are important or useful in the preprocessing pipeline, and how choices made at this step may affect subsequent analyses.

Difficulty level: Beginner
Duration: 11:48
Speaker: : Arnaud Delorme

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 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

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 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

    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

    This lecture covers the linking neuronal activity to behavior using AI-based online detection. 

    Difficulty level: Beginner
    Duration: 30:39

    This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

    Difficulty level: Beginner
    Duration: 1:22:06
    Speaker: : Daniel Buchman

    This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity. 

    Difficulty level: Beginner
    Duration: 1:06:35
    Speaker: : Laura Sikstrom

    Introduction of the Foundations of Machine Learning in Python course - Day 01.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Beginner
    Duration: 35:24
    Speaker: : Elena Trunz

    Optimization for machine learning - Day 02 lecture of the Foundations of Machine Learning in Python course.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Advanced
    Duration: 34:52
    Speaker: : Moritz Wolter

    Linear Algebra for Machine Learning - Day 03 lecture of the Foundations of Machine Learning in Python course.

    High-Performance Computing and Analytics Lab, University of Bonn

    Difficulty level: Advanced
    Duration: 57.45
    Speaker: : Moritz Wolter