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.
In this lesson you will learn about experimental design for EEG acquisition, as well as the first phases of the EEG/ERP data lifecycle.
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.
In this lesson, while learning about the need for increased large-scale collaborative science that is transparent in nature, users also are given a tutorial on using Synapse for facilitating reusable and reproducible research.
This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture provides an introduction to reproducibility issues within the fields of neuroimaging and fMRI, as well as an overview of tools and resources being developed to alleviate the problem.
This lecture provides a historical perspective on reproducibility in science, as well as the current limitations of neuroimaging studies to date. This lecture also lays out a case for the use of meta-analyses, outlining available resources to conduct such analyses.
This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.
This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.
This opening lecture from INCF's Short Course in Neuroinformatics provides an overview of the field of neuroinformatics itself, as well as laying out an argument for the necessity for developing more sophisticated approaches towards FAIR data management principles in neuroscience.
This lesson provides a thorough description of neuroimaging development over time, both conceptually and technologically. You will learn about the fundamentals of imaging techniques such as MRI and PET, as well as how the resultant data may be used to generate novel data visualization schemas.
This lesson gives a brief introduction to the course Neuroscience for Machine Learners (Neuro4ML).
This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This module explores sensation in the brain: what organs are involved, sensory pathways, processing centers, and theories of integration.
This module covers how the brain interacts with the world through motor movements. Motor movements underlie so much of our functioning, our speech, the opening and closing of our eyes, and the beating of our hearts.