Signal Processing for Brain Interfaces

Course Information
TitleΕπεξεργασία Σήματος για Εγκεφαλικές Διεπαφές / Signal Processing for Brain Interfaces
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorNikolaos Laskaris
Course ID600016148

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 eōs sīmera) MF

Registered students: 5
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization217.5

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 éōs sīmera) PF

Registered students: 14
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization217.5

Class Information
Academic Year2019 – 2020
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Total Hours39
Class ID
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Language of Instruction
  • Greek (Instruction, Examination)
General Prerequisites
Digital Signal Processing. Pattern recognition. Computational Intelligence. MATLAB
Learning Outcomes
Cognitive: Gaining a deeper understanding of signal processing techniques. Exposition to current trends in BCI research and applications. Acquaintance with brain activity decoding techniques. Skills: Programming techniques and tools for the design and implementation of BCIs
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Generate new research ideas
  • Be critical and self-critical
Course Content (Syllabus)
Basic principles of Neurophysiology. Evoking and recording brain activity. Digital techniques in brain signal analysis (spike-sorting, spectral and wavelet analysis, nonlinear dynamics, etc.). Spatial filtering and multivariate techniques for multichannel signal processing. Brainwaves decoding in BCI. Applications in clinical rehabilitation and modern paradigms of human-machine interaction.
signal analysis, brain activity decoding
Educational Material Types
  • Notes
  • Slide presentations
  • Book
  • matlab software
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
Course Organization
Laboratory Work45
Reading Assigment60
Student Assessment
In the final mark contibute : 1) the deliverable of a student-specific project (50%) 2) the performance in the analysis and presentation of a journal-article from the recent bibliography (30%) 3) written exams (20%)
Student Assessment methods
  • Written Exam with Short Answer Questions (Summative)
  • Performance / Staging (Summative)
  • Report (Summative)
Additional bibliography for study
Rao R. Brain-Computer Interfacing: An Introduction. Cambridge University Press. 2013 and a collection of tutorial articles.
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