Signal Processing for Brain Interfaces
| Title | Επεξεργασία Σήματος για Εγκεφαλικές Διεπαφές / Signal Processing for Brain Interfaces |
| Code | DMCI204 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Nikolaos Laskaris |
| Common | No |
| Status | Active |
| Course ID | 600016148 |
Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 eōs sīmera) MF
Registered students: 5
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 2 | 1 | 7.5 |
Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 éōs sīmera) PF
Registered students: 14
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 2 | 1 | 7.5 |
| Academic Year | 2019 – 2020 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600153682
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600153682
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=8171
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
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.
Keywords
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
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 30 | ✓ | |||
| Laboratory Work | 45 | ✓ | |||
| Reading Assigment | 60 | ✓ | |||
| Project | 90 | ✓ | |||
| Total | 225 |
Student Assessment
Description
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)
Bibliography
Additional bibliography for study
Rao R. Brain-Computer Interfacing: An Introduction. Cambridge University Press. 2013
and a collection of tutorial articles.
Last Update
05-10-2020
