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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

This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.

Difficulty level: Beginner
Duration: 2:24:35

This lecture will provide an overview of neuroimaging techniques and their clinical applications.

Difficulty level: Beginner
Duration: 45:29
Speaker: : Dafna Ben Bashat

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

Difficulty level: Beginner
Duration: 37:51

This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.

Difficulty level: Beginner
Duration: 40:32

This lesson gives an introduction to simple spiking neuron models.

Difficulty level: Beginner
Duration: 48 Slides
Speaker: : Zubin Bhuyan

This lesson provides an introduction to simple spiking neuron models.

Difficulty level: Beginner
Duration: 48 Slides
Speaker: : Zubin Bhuyan

This presentation accompanies the paper entitled: An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data (see link below to download publication). 

Difficulty level: Beginner
Duration: 4:56

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 lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about. 

Difficulty level: Beginner
Duration: 27:48
Speaker: : Gunnar Blohm

This lecture focuses on how to get from a scientific question to a model using concrete examples. We will present a 10-step practical guide on how to succeed in modeling. This lecture contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded Q&A sessions.

Difficulty level: Beginner
Duration: 29:52
Speaker: : Megan Peters

This lecture formalizes modeling as a decision process that is constrained by a precise problem statement and specific model goals. We provide real-life examples on how model building is usually less linear than presented in Modeling Practice I

Difficulty level: Beginner
Duration: 22:51
Speaker: : Gunnar Blohm

This lecture focuses on the purpose of model fitting, approaches to model fitting, model fitting for linear models, and how to assess the quality and compare model fits. We will present a 10-step practical guide on how to succeed in modeling. 

Difficulty level: Beginner
Duration: 26:46
Speaker: : Jan Drugowitsch

This lecture summarizes the concepts introduced in Model Fitting I and adds two additional concepts: 1) MLE is a frequentist way of looking at the data and the model, with its own limitations. 2) Side-by-side comparisons of bootstrapping and cross-validation.

Difficulty level: Beginner
Duration: 38.17
Speaker: : Kunlin Wei