Course Objective

- The student has a basic knowledge of electrophysiology and the
background of electromyographical signals;
- the student has a basic knowledge of the different ways of collecting
electromyograpical data in various fields of application;
- the student can choose the appropriate method for collecting and
analyzing EMG data in a kinesiological study;
- the student knows the possibilities and limitations of EMG data;
- the student can interpret EMG data in relation to motor control, force
and fatigue;
- the student can identify contamination in EMG data and know which
methods to apply to reduce its effects.

Course Content

In this course, the students are introduced to the electrophysical
background of electromyography (EMG). Subsequently, the course focuses
on methodological aspects of EMG acquisition and analysis, addressing
the potential of this method as well as its pitfalls.

Teaching Methods

lectures 5 x 2 hours
seminars 4 x 1 and 1 x 3 hours
The lectures introduce the following topics:
- EMG amplitude estimation;
- force regulation (motor unit recruitment and firing);
- sources of variability of EMG data;
- electrode types;
- bio-electricity
- volume conduction;
- action potential propagation;
- applications of EMG amplitude estimation;
- applications of EMG frequency content estimation
- optimisation of EMG acquisition and analysis.
- neural control of movement
In the seminars questions of students and the exercises will be

Method of Assessment

2 hours; written test with equally weighted open- ended questions

Entry Requirements

- basic knowledge and understanding of the physiology of muscles and
their control.


Scientific articles and lecture handouts

General Information

Credits 3 EC
Period P5
Course Level 400
Language of Tuition English
Faculty Fac. of Behavioural and Movement Science
Course Coordinator prof. dr. J.H. van Dieen
Examiner prof. dr. J.H. van Dieen
Teaching Staff prof. dr. J.H. van Dieen
dr. N. Dominici

Practical Information

You need to register for this course yourself

Teaching Methods Lecture, Computer lab*

*You cannot select a group yourself for this teaching method, you will be placed in a group.