Neurophysiological signal analysis

Course objectives

Students will be introduced to the basic elements of the neurophysiological signals analysis.

The course deals with different modalities of neurophysiological signals, that are used in daily clinical work and scientific research. During the course students will be introduced to different systems for neurophysiological signal analysis, definitions and elaborations of different ways of collecting and processing neurophysiological signals.

The aim of the course is to provide knowledge about different applications of neurophysiological signals, the specifics of their collection and scientific and clinical requirements for their analysis.

Enrolment requirements and/or entry competences required for the course

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Learning outcomes at the level of the programme to which the course contributes

  • Apply specific knowledge and skills from selected disciplines constituting cognitive science.
  • Integrate insights, methods, and levels of analysis across different disciplines into a unified framework for understanding the human mind and cognition in general.
  • Critically evaluate cognitive science findings and synthesize information to be employed in a collaborative professional environment.
  • Employ cognitive science insights in developing innovative, human-friendly and sustainable technological solutions.
  • Apply interdisciplinary approach in examining phenomena pertaining to cognition.
  • Initiate and sustain innovation activities in an interdisciplinary team.
  • Build communication channels and enable the flow of innovative ideas towards professionals employed in related scientific disciplines and industry.
  • Employ diverse disciplinary tools in exploring and describing the nature of cognitive processes.
  • Plan and track personal professional growth.

Course content (syllabus)

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

Student responsibilities

Regular class attendance and class participation.

Required literature

  • Subasi A. Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques. Academic Press, 2019., ISBN 978-0-12-817444-9

Optional literature

  • Išgum V. i suradnici. Elektrofiziološke metode u medicinskim istraživanjima, Medicinska naklada Zagreb, 2003. - priručnik ISBN 953-176-135-3
  • Šantić A. Biomedicinska elektronika, Školska knjiga, dd., Zagreb 1995., ISBN 953-0-31637-2