This elective will give students a first introduction to "Cognitive
Electrophysiology", in which electrophysiology is used to measure and
understand cognitive functions such as visual perception, attention,
working memory and language in terms of brain processes. The course will
provide students with a rudimentary theoretical and methodological
background in electroencephalography (EEG) and to some extent
magnetoencephalography (MEG), enabling them to better understand and
interpret currently cutting-edge analysis techniques that are
increasingly being applied to EEG, MEG, and other electrophysiological
signals in cognitive neuroscience.
At the end of the course, students:
• Know the historical and theoretical background of cognitive
electrophysiological signals such as EEG: where does EEG come from and
what neural processes does it capture? What are its strengths, what are
• Understand the basic steps involved in setting up an EEG experiment
• Have obtained a first hands on introduction to EEG acquisition and
know the steps involved in acquiring EEG
• Are able to perform rudimentary EEG analyses, including pre-processing
and computing an ERP
• Are able to understand and interpret most basic and some advanced EEG
• The neurophysiological basis of EEG and MEG: history, relationship
with neural activity, source localization, the inverse problem
• Preprocessing of electrophysiological signals: what is a ‘signal’?
re-referencing, filtering, artifact rejection
• Basic analyses: Event Related Potentials (ERPs), the multiple
• Important classical findings using ERPs in the context of cognitive
functioning: ERP components involved in visual and/or language
processing such as the C1, P1, N2, P3, N400, P600; lateralized
components involved in action selection, attention and memory such as
the LRP, N2Pc, CDA. The functional meaning of ERP components, and how to
set up EEG experiment.
• Rudimentary time-frequency analysis: Time-frequency decomposition
using fourier and wavelets, relationship between ERPs and the
time-frequency domain, total power versus induced power
• Multivariate statistics: brain reading by obtaining classification
accuracy through decoding methodology, train-test analysis approaches,
investigating cortical stability through Generalization Across Time
• Building forward encoding models that specify the relationship between
cortical activity and some continuous cognitive variable, allowing one
to predict cognitive contents or cortical activations maps for ‘new’
conditions for which no data exists
Form of tuition
Lectures, computer practicals and lab demos.
Type of assessment
Every lecture starts with a mini-exam.
The final exam consists of 10 open questions.
The final grade consists of:
75% Final exam
15% participation in practicals (percentage finished practicals,
10% Average of the mini-exams that are given at the start of every
Lecturers and practicals are obligatory. If you miss more than two
practicals, you will not get a grade for the course.
Selected parts from (tentative, including but not limited to, a full
list will be provided at the start of the course):
• Cohen, M. X. (2017). Where Does EEG Come From and What Does It Mean?
Trends in Neurosciences.
• Woodman GF (2010) A Brief Introduction to the Use of Event-Related
Potentials (ERPs) in Studies of Perception and Attention. Attention
Perception & Psychophysics 72(8):2031–2046.
• Luck SJ (2014) An Introduction to the Event-Related Potential
Technique (MIT Press).
• Fahrenfort, J. J., Scholte, H. S., & Lamme, V. A. F. (2007). Masking
disrupts reentrant processing in human visual cortex. Journal of
Cognitive Neuroscience, 19(9), 1488–1497.
• Cohen MX (2014) Analyzing Neural Time Series Data (MIT Press)
• Grootswagers T, Wardle SG, Carlson TA (2016) Decoding dynamic brain
patterns from evoked responses: A tutorial on multivariate pattern
analysis applied to time-series neuroimaging data. arXiv.
• King JR, Dehaene S (2014) Characterizing the dynamics of mental
representations: the temporal generalization method. Trends Cogn Sci 18
• Fahrenfort, J. J., Grubert, A., Olivers, C. N. L., & Eimer, M. (2016).
Multivariate EEG analyses support high-resolution tracking of
feature-based attentional selection. bioRxiv.
Successful completion of the course Brain imaging (1st yr course)
This course is only available for students who have successfully
completed the course "Brain Imaging".