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The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.

Difficulty level: Beginner
Duration: 55:36
Speaker: : Elissa Chesler

This lesson provides an introduction to the lifecycle of EEG/ERP data, describing the various phases through which these data pass, from collection to publication.

Difficulty level: Beginner
Duration: 35:30

In this lesson you will learn about experimental design for EEG acquisition, as well as the first phases of the EEG/ERP data lifecycle. 

Difficulty level: Beginner
Duration: 30:04

This lesson provides an overview of the current regulatory measures in place regarding experimental data security and privacy. 

Difficulty level: Beginner
Duration: 31:00

In this lesson, you will learn the appropriate methods for collection of both data and associated metadata during EEG experiments.

Difficulty level: Beginner
Duration: 29:14

This lesson goes over methods for managing EEG/ERP data after it has been collected, from annotation to publication. 

Difficulty level: Beginner
Duration: 39:25

In this final lesson of the course, you will learn broadly about EEG signal processing, as well as specific applications which make this kind of brain signal valuable to researchers and clinicians. 

Difficulty level: Beginner
Duration: 34:51

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

Difficulty level: Beginner
Duration: 15:32
Speaker: : Arnaud Delorme

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

Difficulty level: Beginner
Duration: 6:53
Speaker: : Arnaud Delorme

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

Difficulty level: Beginner
Duration: 10:56
Speaker: : Arnaud Delorme

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

Difficulty level: Beginner
Duration: 6:06
Speaker: : Arnaud Delorme

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

Difficulty level: Beginner
Duration: 9:20
Speaker: :

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. 

Difficulty level: Beginner
Duration: 10:55
Speaker: : Arnaud Delorme

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

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
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