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Lecturer(s)
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Zýka Stanislav, doc. Ing. Ph.D.
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Course content
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Neuroinformatics: from brain to data Brain, neurons, and computational models Methods for measuring brain activity Design of neuroscientific experiments Electroencephalography and EEG experiments. EEG preprocessing and quality control Analysis and interpretation of EEG data Neurodata, metadata, standards, and reproducible research Machine learning for neuroscientific data Deep learning and modern AI in neuroscience Brain-computer interfaces and neurotechnology Interpretation of neuroscientific results, ethics, and current trends
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Learning activities and teaching methods
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- Contact hours
- 26 hours per semester
- Practical training (number of hours)
- 26 hours per semester
- Team project (50/number of students)
- 60 hours per semester
- Preparation for an examination (30-60)
- 30 hours per semester
- unspecified
- 10 hours per semester
- Presentation preparation (report) (1-10)
- 4 hours per semester
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| prerequisite |
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| Knowledge |
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| - be familiar with high school mathematics, - understand the basic principles of working with data and the fundamental concepts of artificial intelligence, - understand the basic principles of programming and be able to read and modify simple program code. |
| Skills |
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| - apply knowledge of high school mathematics in common tasks, - explain the basic principles of working with data and the fundamental concepts of artificial intelligence, - read, run, and make simple modifications to program code. |
| Competences |
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| N/A |
| N/A |
| N/A |
| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| - explain the basic principles of the organization and functioning of the nervous system and their relationship to selected computational models, - characterize and compare basic methods for measuring brain activity, including their capabilities and limitations, - describe the principles of neuroscientific experiment design and the life cycle of neuroscientific data, - characterize the basic procedures used for processing and analysing neurophysiological data, particularly EEG, - evaluate the possibilities and limitations of machine learning and deep learning methods for the analysis of neuroscientific data, - identify and relate data and metadata standards, FAIR principles, and the principles of open and reproducible science, - explain and distinguish the basic principles and paradigms of brain-computer interfaces and their areas of application, - critically interpret the results of neuroscientific data analyses and evaluate selected neurotechnologies and related ethical issues. |
| Skills |
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| - apply a selected neuroinformatics data or metadata standard to describe and organize neuroscientific data, - load, visualize, and assess the quality of neurophysiological data, - preprocess and analyse electroencephalographic data using appropriate computational tools, - apply and evaluate a simple machine learning method on neuroscientific data, - design, implement, and document a reproducible workflow for processing neuroscientific data, - interpret and present the results of a neuroinformatics experiment with regard to the methods used and their limitations. |
| Competences |
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| N/A |
| N/A |
| - read and critically evaluate a relevant scientific text |
| teaching methods |
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| Knowledge |
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| Lecture with visual aids |
| Lecture supplemented with a discussion |
| Multimedia supported teaching |
| Textual studies |
| Project-based instruction |
| Students' portfolio |
| Task-based study method |
| Skills |
|---|
| Task-based study method |
| Practicum |
| Project-based instruction |
| Individual study |
| Students' portfolio |
| Laboratory work |
| Competences |
|---|
| Lecture |
| Lecture with visual aids |
| Practicum |
| Project-based instruction |
| Individual study |
| Students' portfolio |
| Task-based study method |
| assessment methods |
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| Knowledge |
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| Combined exam |
| Project |
| Skills |
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| Project |
| Skills demonstration during practicum |
| Group presentation at a seminar |
| Competences |
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| Combined exam |
| Project |
| Skills demonstration during practicum |
| Group presentation at a seminar |
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Recommended literature
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František Koukolík. Já : o mozku, vědomí a sebeuvědomování 2., přeprac. a dopl. vyd.. Praha: Karolinum, 2013. ISBN 978-80-246-2249-1.
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Jiří Horáček, Cyril Höschl, Ladislav Kesner, Filip Španiel. Mozek a jeho člověk, mysl a její nemoc. Praha: Galén, 2016. ISBN 9788074922831.
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Luck, Stephen J. An introduction to the event-related potential technique. Cambridge: MIT Press, 2005. ISBN 978-0-262-62196-0.
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Olivier Colliot. Machine Learning for Brain Disorders. 2023, ISSN 1940-6045.
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