Neuroimaging II: Electrophysiological Methods
Course infoSchedule
Course moduleSOW-DGCN39
Credits (ECTS)6
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Cognitive Neuroscience;
dr. E.G.G. Maris
Other course modules lecturer
dr. E.G.G. Maris
Other course modules lecturer
Contactperson for the course
dr. E.G.G. Maris
Other course modules lecturer
dr. J.M. Schoffelen
Other course modules lecturer
Academic year2017
SEM2  (05/02/2018 to 13/07/2018)
Starting block
Course mode
Registration using OSIRISYes
Course open to students from other facultiesNo
Waiting listNo
Placement procedure-

This course is about the analysis of EEG- and MEG-data. The objective is to introduce the student to the most important analysis methods for this type of data.


EEG- and MEG-signals produced by neurophysiological processes. The signals that are measured using EEG-electrodes or MEG-sensors are produced by neurophysiological processes in the neuropil. In this part of the course, we describe these processes, the electrical currents they produce, and how these currents produce measurable EEG- and MEG-signals.

Signal processing of electrophysiological data. Often, the relevant aspect in the electrophysiological data is a modulation of oscillatory components. To identify these components, we need a representation in the frequency domain. In this part of the course, we present the frequency domain methods that are most prominent in current research.

Source reconstruction. The gold standard in cognitive neuroscience are measurements of electrophysiological signals at locations in the brain from where they originate physiologically. Unfortunately, in human cognitive neuroscience, we can only measure these signals at some distance from their physiological origin, via the EEG or the MEG. Source reconstruction techniques, are then used to infer a signal at the source level (the brain's grey matter) from an observed signal at the sensor level. In this part of the course, we give an introduction to these methods.

Statistical testing of electrophysiological data. One of the challenges in the statistical analysis of electrophysiological data is the high dimensionality of this type of data (many channels, many time points, and many frequencies). For this type of data, nonparametric methods are much better suited than the classical parametric methods. In this part of the course, we give an introduction to these nonparametric methods.

Additional comments
COURSE: February 5 – June 25, 2018; Monday 10.45-12.30
LOCATION: t.b.a.

Test information
EXAM: Friday July 13, 2018; 10.30-13.30
TYPE OF EXAM: closed-book exam with open questions
NOTE: enrollment for a course automatically registers you for its exam. If you don't want to do the first exam you have to deregister for the exam in OSIRIS, but do not forget to sign up for the retake in OSIRIS.

Taking the exam for this course is only allowed after the course DGCN09 (Advanced math) has been passed successfully. If you have sufficient knowledge of mathematics you may request an exemption from this rule from the Examination Board.

Contact information
Dr. E. Maris

Required materials
Book chapters and journal articles.

Instructional modes
Attendance MandatoryYes

Attendance MandatoryYes

This course involves a mixture of lectures and feedback sessions in which the students ask questions about texts and assignments they have prepared in advance. All topics will be introduced in part by Matlab computer excercises which have to be prepared in advance.

closed-book exam with open questions
Test weight1
OpportunitiesBlock HER, Block SEM2