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

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

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

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. 

This lecture gives an introduction to the FAIR (findability, accessibility, interoperability, and reusability) science principles and examples of their application in neuroscience research. 

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.

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

This lecture summarizes the concepts introduced in Model Types I and further explains how models can be used answer different scientific questions. 

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.

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. 

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. 

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.

This lecture provides an overview of the generalized linear models (GLM) course, originally a part of the Neuromatch Academy (NMA), an interactive online summer school held in 2020. NMA provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience". 

This lecture further develops the concepts introduced in Machine Learning I. This lecture is part of the Neuromatch Academy (NMA), an interactive online computational neuroscience summer school held in 2020.

This lesson provides an overview of the process of developing the TVB-NEST co-simulation on the EBRAINS infrastructure, and its use cases.

This lecture introduces the core concepts of dimensionality reduction.

This lecture covers the application of dimensionality reduction applied to multi-dimensional neural recordings using brain-computer interfaces with simultaneous spike recordings.

This is the first of a series of tutorials on fitting models to data. In this tutorial, we start with simple linear regression, using least squares optimization.

In this tutorial, we will use a different approach to fit linear models that incorporates the random 'noise' in our data.