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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 lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

    Difficulty level: Beginner
    Duration: 1:16:04

    This talk provides an overview of the FAIR-aligned efforts of MATLAB and MathWorks, from the technological building blocks to the open science coordination involved in facilitating greater transparency and efficiency in neuroscience and neuroinformatics. 

    Difficulty level: Beginner
    Duration: 15:41
    Speaker: : Vijay Iyer

    This tutorial is part 1 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are part of the same YouTube video; part 1 ends at 17:42. 

    Difficulty level: Beginner
    Duration: 17:42
    Speaker: : Edureka

    This tutorial is part 2 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are the same YouTube video. The portion related to this tutorial begins at 17:43. 

    Difficulty level: Beginner
    Duration: 1:32:59
    Speaker: : Edureka

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

    Difficulty level: Beginner
    Duration: 6:10
    Speaker: : MATLAB®

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

    Difficulty level: Beginner
    Duration: 15:10
    Speaker: : MATLAB®

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

    Difficulty level: Beginner
    Duration: 2:49
    Speaker: : MATLAB®

    This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.

    Difficulty level: Beginner
    Duration: 6:10
    Speaker: : MATLAB®

    This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

    Difficulty level: Beginner
    Duration: 6:27
    Speaker: : MATLAB®

    This brief tutorial goes over how you can easily work with big data as you would with any size of data.

    Difficulty level: Beginner
    Duration: 3:55
    Speaker: : MATLAB®

    In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.

    Difficulty level: Beginner
    Duration: 3:52
    Speaker: : MATLAB®

    This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

    Difficulty level: Beginner
    Duration: 1:16:36
    Speaker: : Tal Yarkoni

    This tutorial teaches users how to use Pandas objects to help store and manipulate various datasets in Python. 

    Difficulty level: Beginner
    Duration: 1:21:40
    Speaker: : Tal Yarkoni