Course ObjectiveAt 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
Course ContentThis course 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.
Themes that will be covered:
-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
I-mportant 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 temporal generalization
-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
Teaching MethodsLectures, computer practicals, and lab demos.
Method of AssessmentEvery lecture (except the first one) starts with a 4-question
multiple-choice mini-exam about the study material of the previous
lecture. The final exam consists of 7 multi-part open questions.
The final grade consists of:
10 startup points (these result in the lowest grade 1 if no more points
are attained during the course)
67 points for the final exam
14 points for participation in practicals (2 points for every attended
practical, attendance obligatory)
9 points total for the the mini-exams that are given at the start of
every lecture (1.5 points for every exam)
Your final grade in the Dutch rating system (grade from 1 to 10) is
computed by adding up all points for the course and dividing by 10. This
means that the final grade ranges from 1 (no more points attained during
the course) and 10 (all points attained during the course).
Lecturers and practicals are obligatory. If you miss more than two
practicals, you will not get a grade for the course.
LiteratureSelected 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, 40(4), 208–218.
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) -> selected chapter will be provided.
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, S. G., & Carlson, T. A. (2017). Decoding
Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate
Pattern Analysis Applied to Time Series Neuroimaging Data. Journal of
Cognitive Neuroscience, 29(4), 677–697.
Jackson, A. F., & Bolger, D. J. (2014). The neurophysiological bases of
EEG and EEG measurement: a review for the rest of us. Psychophysiology,
51(11), 1061–1071. http://doi.org/10.1111/psyp.12283
King, J. R., & Dehaene, S. (2014). Characterizing the dynamics of mental
representations: the temporal generalization method. Trends in Cognitive
Sciences, 18(4), 203–210. http://doi.org/10.1016/j.tics.2014.01.002
Fahrenfort, J. J., Grubert, A., Olivers, C. N. L., & Eimer, M. (2017).
Multivariate EEG analyses support high-resolution tracking of
feature-based attentional selection. Scientific Reports, 7(1), 1886.
|Language of Tuition||English|
|Faculty||Fac. of Behavioural and Movement Science|
|Course Coordinator||dr. J.J. Fahrenfort|
|Examiner||dr. J.J. Fahrenfort|
dr. J.J. Fahrenfort
You need to register for this course yourself
|Teaching Methods||Computer lab, Lecture|