Universität Wien
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160161 PS Implementation and Analysis of fNIRS Experiments (2023S)

fNIRS Methoden Kurs

6.00 ECTS (2.00 SWS), SPL 16 - Musikwissenschaft und Sprachwissenschaft
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 40 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Die Abschlusseinheit findet wieder digital statt, voraussichtlich im Mai.

  • Wednesday 12.04. 09:30 - 11:00 Digital
  • Wednesday 12.04. 14:30 - 16:00 Digital
  • Thursday 13.04. 09:30 - 11:00 Digital
  • Thursday 13.04. 14:30 - 16:00 Digital
  • Friday 14.04. 09:30 - 11:00 Digital
  • Friday 14.04. 14:30 - 16:00 Digital
  • Monday 24.04. 11:15 - 14:45 Seminarraum 3 Sensengasse 3a 1.OG
  • Monday 24.04. 16:45 - 19:45 Seminarraum 2 Sensengasse 3a 1.OG
  • Tuesday 25.04. 09:45 - 12:45 Seminarraum 7 Sensengasse 3a 2.OG

Information

Aims, contents and method of the course

Aims
To learn the basics of the fNIRS technique, its physical principles, how to best implement an fNIRS experiment and how to analyze fNIRS data. The final goal of this course is that you will be able to analyze a dataset of fNIRS data.

Contents
Students will learn the physical principles at the basis of fNIRS and how an fNIRS acquisition takes place. Then students will gain theoretical knowledge of fNIRS analysis methods, which they will then apply practically on their own laptop using some example datasets. Students will then try to implement their own pipeline, working in small groups, on a new dataset and justify their choices based on the learnt theoretical knowledge. We will use Matlab software and Homer3 and AtlasViewer as fNIRS analysis toolboxes, which can be freely downloaded from here: https://openfnirs.org/software/homer/.

Methods
Theoretical lectures will be interleaved with practical sessions where students will learn how to implement fNIRS data analysis. Some of these practical sessions will be performed in small groups, with students learning whilst doing. The course will be conducted in English.

Assessment and permitted materials

Homework: the homework consists in taking a provided fNIRS dataset and analyzing the dataset replying to the questions asked in the homework. The analysis pipeline in Matlab can be done in small groups. The scientific report showing the pipeline employed, the results, the answers to the practical and theoretical questions asked in the homework should be done individually.

Minimum requirements and assessment criteria

Presence in class is required for this practical course to work.
The overall grade consists in two parts: evaluation of the analysis pipeline used to solve the homework (50%) and evaluation of the responses to the theoretical questions asked in the homework (50%).

Examination topics

The relevant literature is provided in the reading list and is supposed to help students understanding the basic principles of fNIRS and fNIRS data analysis. The slides of the lectures will be the main source to study.

Reading list

Brigadoi, S. & Cooper, R.J.. Diffuse Optical Imaging, in Bloomfield, P.S., Brigadoi, S., Rizzo, G. & Veronese, M., Basic Neuroimaging: A Guide to the Methods and Their Applications. Second Edition: CreateSpace Independent Publishing Platform, 2021.
Ayaz, H., Baker, W. B., Blaney, G., Boas, D. A., Bortfeld, H., Brady, K., ... & Zhou, W. (2022). Optical imaging and spectroscopy for the study of the human brain: status report. Neurophotonics, 9(S2), S24001.
Yücel, M. A., Lühmann, A. V., Scholkmann, F., Gervain, J., Dan, I., Ayaz, H., ... & Wolf, M. (2021). Best practices for fNIRS publications. Neurophotonics, 8(1), 012101.

Association in the course directory

MA1-M3

Last modified: Th 11.05.2023 11:27