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 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.
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.
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.
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.
This lecture covers the linking neuronal activity to behavior using AI-based online detection.
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.
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.
Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
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