Bachelor in Robotics Engineering (Plan: 478 - Estudio: 381)
Coordinating teacher: GOMEZ CID, LIDIA
Department assigned to the subject: Bioengineering Department
Type: Electives
ECTS Credits: 3.0 ECTS
Course: 4º
Semester:
Requirements (Subjects that are assumed to be known)
It is recommended that students have completed the following courses:
- Signals, systems, and circuits
- Advanced sensors and measurement techniques
- Biophysics 1: Molecular, cellular, and tissue biology
Description of contents: programme
1 Neurophysiology
2 Brain signals: recording and Imaging
3 Internal and external brain stimulation
4 Neural Signal processing
5 Machine learning for brain signals
6 Main types of Brain Machine Interfaces
7 Clinical and practical applications of Brain Machine Interfaces
8 Ethics of Brain Machine Interfaces
Learning activities and methodology
THEORETICAL PRACTICAL CLASSES.
Knowledge and concepts students must acquire. Receive course notes and will have basic reference texts. Students partake in exercises to resolve practical problems.
TUTORING SESSIONS.
Individualized attendance (individual tutoring) or in-group (group tutoring) for students with a teacher. Subjects with 3 credits have 2 hours of tutoring/ 100% on- site attendance.
STUDENT INDIVIDUAL WORK OR GROUP WORK.
Subjects with 3 credits have 49 hours/0% on-site.
WORKSHOPS AND LABORATORY SESSIONS.
Subjects with 3 credits have 4 hours with 100% on-site instruction.
Assessment System
% end-of-term-examination/test 60
% of continuous assessment (assigments, laboratory, practicals...) 40
Bin He, Editor. Neural Engineering (Second Edition). Springer, New York Heidelberg Dordrecht London. 2013, ISBN 978-1-4614-5226-3, ISBN 978-1-4614-5227-0 (eBook), DOI 10.1007/978-1-4614-5227-0.
Paul Miller, 2018, ISBN: 9780262038256.. An Introductory Course in Computational Neuroscience. MIT Press, Cambridge, MA. 2018
The course syllabus may change due academic events or other reasons.
