This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.
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 module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more.
Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.
This compilation is courtesy of freeCodeCamp.
This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.
This lesson was created by RootMath.
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.
In this lecture, attendees will learn how Mutant Mouse Resource and Research Center (MMRRC) archives, cryopreserves, and distributes scientifically valuable genetically engineered mouse strains and mouse ES cell lines for the genetics and biomedical research community.
This lesson provides an overview of Neurodata Without Borders (NWB), an ecosystem for neurophysiology data standardization. The lecture also introduces some NWB-enabled tools.
This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.
This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.