Neurophysiological Signal Analysis

Data is displayed for the academic year: 2025./2026.

Lecturers

Prof.
Mario Cifrek
PhD
Adj. Assoc. Prof.
Magdalena Krbot Skorić
PhD

Lectures

Prof.
Alan Jović
PhD
Prof.
Ervina Bilić
PhD
Prof.
Željka Petelin Gadže
PhD
Asst. Prof.
Barbara Barun
PhD
Prof.
Igor Lacković
PhD
Tereza Gabelić
PhD
Prof.
Mario Habek
PhD

Course Description

Introduction to neurophysiological signal analysis. Signal processing basics. Time-frequency analysis of neurophysiological signals. Neurophysiological signal processing in psychophysiological studies . Basic principles of electroencephalography (EEG). Applications of signal analysis methods in neurophysiological diagnostic procedures (EEG). Basic principles of evoked potentials (EP). Applications of signal analysis methods in neurophysiological diagnostic procedures (EP). Basic principles of polisomnography. Basic principles of electromyography (EMG). Applications of signal analysis methods in neurophysiological diagnostic procedures (EMG). Basic principles of autonomic nervous system testing (ANS). Applications of signal analysis methods in neurophysiological diagnostic procedures (ANS). Application of neurophysiological signal analysis in the cognitive function testing. Statistical methods applied in the neurophysiological signals analysis. Machine learning applied to neurophysiological signals. Applications of machine learning methods on neurophysiological signals. Deep learning of neurophysiological signals.

Prerequisites

Knowledge about the basics of the neurophysiology and the biomedical signal analysis.

Study Programmes

University graduate
[FER3-HR] Biomedical Engineering - study
Elective Courses (2. semester) (4. semester)

Learning Outcomes

  1. To identify what type of neurophysiological signals are required for use for specific clinical (therapeutic and diagnostic) purposes
  2. Identify what type of neurophysiological signals are required for use for specific clinical (therapeutic and diagnostic) purposes.
  3. Classify appropriate methods of analysis depending on the type of application (different groups of subjects - different pathologies, different methods of data collection).
  4. Create your own system for neurophysiological signal analysis.
  5. Relate the results obtained by the analysis of neurophysiological signals with the metabolic and morphological background in a certain type of pathology.
  6. Compare the results of different data processing methods on collected data from the real world

Forms of Teaching

Lectures

The lectures are divided into units from the field of clinical neurophysiology.

Seminars and workshops

Students work independently or in groups on a topic from the field.

Laboratory

Practicals in the neurophysiological laboratory.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 0 % 10 % 0 % 10 %
Seminar/Project 0 % 30 % 0 % 30 %
Mid Term Exam: Written 50 % 30 % 0 %
Final Exam: Written 50 % 30 %
Exam: Written 50 % 60 %

Week by Week Schedule

  1. Lectures: Introduction to neurophysiological signal analysis
  2. Lectures: Signal processing basics
  3. Lectures: Time-frequency analysis of neurophysiological signals
  4. Lectures: Neurophysiological signal processing in psychophysiological studies
  5. Lectures: Basic principles of electroencephalography (EEG), Laboratory: Applications of signal analysis methods in neurophysiological diagnostic procedures (EEG)
  6. Lectures: Basic principles of evoked potentials (EP), Laboratory: Applications of signal analysis methods in neurophysiological diagnostic procedures (EP)
  7. Lectures: Basic principles of polisomnography
  8. Lectures: Midterm exam, Laboratory: Midterm exam
  9. Lectures: Basic principles of electromyography (EMG), Laboratory: Applications of signal analysis methods in neurophysiological diagnostic procedures (EMG)
  10. Lectures: Basic principles of autonomic nervous system testing (ANS), Laboratory: Applications of signal analysis methods in neurophysiological diagnostic procedures (ANS)
  11. Lectures: Application of neurophysiological signal analysis in the cognitive function testing
  12. Lectures: Statistical methods applied in the neurophysiological signals analysi
  13. Lectures: Machine learning applied to neurophysiological signals, Laboratory: Applications of machine learning methods on neurophysiological signals
  14. Lectures: Deep learning of neurophysiological signals
  15. Lectures: Final exam, Laboratory: Final exam

Literature

(.), Išgum V. i suradnici. Elektrofiziološke metode u medicinskim istraživanjima, Medicinska naklada Zagreb, 2003. - priručnik ISBN 953-176-135-3,
Šantić A. (1995.), Biomedicinska elektronika, Školska knjiga, dd., Zagreb
Subasi A. (2019.), Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques., Academic Press

General

ID 261438
  Summer semester
5 ECTS
L1 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

90 Excellent
80 Very Good
70 Good
50 Sufficient

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