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About this course

The course is designed to provide students and researchers with a solid understanding of functional Near-Infrared Spectroscopy (fNIRS) as a relatively new tool to measure brain activity and will emphasize both theoretical knowledge and practical skills of fNIRS. The students will gain expertise in the underlying principles of fNIRS, its instrumentation, and various analytical approaches. The primary goal is to empower students with the knowledge of this additional neuroimaging tool to design and execute advanced experiments, interpret fNIRS data effectively, and contribute to cutting-edge research in neuroscience and related fields.

Learning outcomes

Upon completion of the fNIRS course, students should be able to:

Knowledge and understanding:

  • Explain the principles of fNIRS and its applications in neuroscience or related fields
  • Describe the preprocessing steps to remove noise in fNIRS signals

Competence and skills

  • Perform an fNIRS experiment
  • Conduct fundamental fNIRS processing/analysis using different methods

Judgement and approach

  • Interpret fNIRS data with regard to brain structure and function
  • Design fNIRS experiments and discuss how fNIRS can be integrated with other lab-based systems (i.e., mobility systems)
Prerequisites for participation

Educational background or research experience in relevant fields such as neurosciences, psychology, medicine, biomedicine, medical physics, medical imaging, computational biology, or any humanistic discipline employing neuroimaging as an experimental tool.  Although not a prerequisite, it is an advantage to have a basic understanding of different statistical methods and programming skills in MATLAB, Python and R.

Necessary language skill
English
Semester(s) in which the module takes place
Autumn Semester 2024
Course type
Lecture + Practical
Course level
Doctoral
Course start date
11 Nov 2024
Course end date
15 Nov 2024
Apply between
Application start date
15 Apr 2024
-
Application end date
30 May 2024
Teaching mode
Local
Local attendance
Required
Workload in ECTS
1.5
Graded
Yes
Person in charge of module
Lucian Bezuidenhout
Email address
lucian.bezuidenhout@ki.se
Local module ID
H1F6004