Course ObjectiveThe course "Rhythms of the Brain" is focused on measuring, analyzing and
interpreting the functional role of neuronal
oscillations in humans.
At the end of the course the student should be able to:
1. Explain how the human brain generates scalp
electroencephalographic (EEG) signals, both ongoing oscillations and
event-related potentials (ERPs).
2. Acquire practical experience with EEG (i.e., measure EEG, perform
quantitative and statistical analysis to draw conclusions about the
relation between brain activity and cognition/behavior, and present the
results on a poster).
3. Explain key concepts of complex-systems science that have gained
acceptance in the cognitive and behavioral neurosciences.
4. Apply state-of-the-art complexity-analysis techniques to EEG
data and perceptual/behavioral time series, and
5. …understand how these techniques can be applied in fundamental
science and applied medical fields, e.g., for clinical trials and
6. Explain the advanced techniques that estimate brain sources from
the EEG signals, and outline the possibilities and limitations based on
7. Explain the rationale of so-called "integrated biomarkers" based on
machine learning, use
specialized toolboxes to compute them and critically reflect on the pros
and cons of this approach to functionally assess the state of a human
brain based on the rhythms that it generates.
Course ContentUnderstanding the complexity of the human brain and mind is one of the
greatest scientific challenges of the 21st century. To address these
challenges, researchers increasingly adopt theories and methods used to
study complexity in other natural systems. In this course, we give you a
solid conceptual understanding of "complexity" and tools to study the
complexity of the human brain through quantitative analysis of the brain
rhythms that it generates and the variability in cognitive and
We consider it critical that students gain an in-depth
understanding of the analytical tools in order to properly use and
interpret the outcome of the different analysis techniques. This is
achieved by covering the theory in the lectures followed by tutorials in
the computer rooms. The concepts of "critical dynamics" and power-law
scaling behavior are carefully explained in the context of time-series
analysis tools, generating mechanisms, and functional implications. Key
concepts of complex networks and analytical tools to characterize them
based on M/EEG data are also covered.
Another important component of the course is to teach you how to perform
high-density EEG recordings of spontaneous brain activity during
resting-state conditions and cognitive tasks and to analyze these
signals with classical as well as modern complexity algorithms. You will
work in small groups to record, analyze and present both data on EEG and
its cognitive/behavioral correlates at the end of the course.
Finally, the importance of non-stimulus driven brain activity and
cognition for brain-related disorders such as depression, dementia,
insomnia or attention deficit and hyperarousal disorder is discussed in
the context of normal variation in biomarkers and the associated
challenges in objective diagnosis, prognosis, and treatment selection.
We explain how data-mining and -classification techniques from
artificial intelligence can be used to integrate information from
multiple biomarker algorithms to increase the accuracy of clinically
relevant functional assessments. While the course is focused on
understanding variability in human cognition and behavior in health and
disease, the concepts and tools equally apply to research on common
Teaching MethodsThe study credits amount to 168 hours of study, which are divided
approximately as follows:
Activity Hours of study
Lectures (l) 20
Self study (literature and lecture sheets) 38
Practicals in EEG lab (Prac) 8
Computer practicals and project assignment (A) 36
Journal club (Pres) 8
Poster preparation (A) 18
Preparation for exams (poster and written) 40
Method of AssessmentAnalysis and making research poster (R, 15%)
Presentation of research poster (Pres, 25%)
Written examination (E: 60%)
Compensation is not possible for any of these assessments.
LiteratureNikulin VV, Linkenkaer-Hansen K, Nolte G, Lemm S, Müller KR, Ilmoniemi
RJ, Curio G. A novel mechanism for evoked responses in the human brain.
Eur J Neurosci. 2007;25:3146–54.
Mazaheri A, Jensen O. Asymmetric amplitude modulations of brain
oscillations generate slow evoked responses. J Neurosci 2008;28:7781–7.
Jensen O, van Dijk H, Mazaheri A. Amplitude asymmetry as a mechanism for
the generation of slow evoked responses. Clin Neurophysiol 2010.
Nikulin VV, Linkenkaer-Hansen K, Nolte G, Curio G. Non-zero mean and
asymmetry of neuronal oscillations have different implications for
evoked responses. Clin Neurophysiol 2010;121:186–93.
Hardstone R, Poil S-S, Schiavone G, Jansen R, Nikulin VV, Mansvelder HD,
Linkenkaer-Hansen K. Detrended fluctuation analysis: A scale-free view
on neuronal oscillations. Frontiers in Physiology. 3:450.
Annotated sheets from lectures
Target AudienceMasters and PhD students with interest in human brain function in
general and EEG methodology in particular.
Additional Informationdr. K. Linkenkaer Hansen with guest lectures of dr. D.J.A. Smit
|Language of Tuition||English|
|Faculty||Faculty of Science|
|Course Coordinator||dr. K. Linkenkaer Hansen|
|Examiner||dr. K. Linkenkaer Hansen|
dr. K. Linkenkaer Hansen
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Study Group, Computer lab, Practical|