Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
After this course the student can
(1) list approaches for analyzing and modeling functional brain imaging data
(2) explain the difference between decoding and encoding models
(3) analyze brain imaging data using different approaches
(4) choose appropriate analysis or modeling approach to new data
(5) critically review scientific literature on “brain decoding”.
Schedule: 12.01.2021 - 17.02.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Linda Henriksson
Teacher in charge (applies in this implementation): Linda Henriksson
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
This course gives an overview of advanced approaches for analyzing and modeling human brain imaging data. The student will learn the main differences between univariate and multivariate analyses, and between encoding and decoding models. The students will also apply the different techniques to real data. The course will also give basic knowledge about the functional organization of the human visual system which will be used as a case example of how the different techniques are used in the context of neuroscientific questions.
Assessment Methods and Criteria
Project works, homework, portfolio, exam.
Lectures/contact teaching 18 h, Reading and homework 36 h, Project works 60 h, Compiling a portfolio 8 h, Getting ready for exam 8 h, Exam 3 h (Total: 133 h)
Lecture slides, book chapters, scientific articles.
Recommended: NBE-E4045 - Functional Brain Imaging
- Teacher: Linda Henriksson