Checking date: 24/04/2024


Course: 2024/2025

Brain Machine Interfaces
(19902)
Bachelor in Biomedical Engineering (Plan: 522 - Estudio: 257)


Coordinating teacher:

Department assigned to the subject:

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Learning Outcomes
K6. To understand the existing techniques in signal processing and the theoretical bases of electrical circuits and dynamic systems that allow the analysis and conceptual design of electronic devices to solve problems in biology and medicine. K11. To understand the most common concepts and techniques in the obtention and processing of biomedical images, as well as artificial vision, and to apply them to the resolution of problems of biological and medical interest, with special emphasis in the diagnosis by medical imaging. K12. To understand the techniques used in the design of medical devices and the instruments that compose them, allowing their development for medical applications, such as surgical instruments, electromechanical microdevices, robots and micro- and nanometric-sized biosensors. S3. To analyze and synthesize basic problems related to bioengineering and biomedical sciences, solving them with initiative, appropriate decision making and creativity and communicating solutions efficiently, including social, ethical, health and safety, environmental, economic and industrial implications. S5. To analyse scientific and technical information for decision-making in the field of biomedical engineering by keeping abreast of new developments S6. To solve mathematical, physical, chemical, biological and biochemistry problems that may arise in biomedical engineering, knowing how to interpret the results obtained and reach informed conclusions. C3. Be able to transmit knowledge both orally and in writing, to a specialised and non-specialised audience, working in multidisciplinary and international teams.
Description of contents: programme
Neurophysiology. Brain signals: recording and imaging. Brain stimulation, internal and external. Neural signal processing. Machine learning for brain signals. Main types of brain-machine interfaces. Clinical and practical applications of brain-machine interfaces. Ethics of Brain-Machine Interfaces.
Learning activities and methodology
LEARNING ACTIVITIES: FACE-TO-FACE CLASSES: REDUCED (WORKSHOPS, SEMINARS, CASE STUDIES) LABORATORY SESSION STUDENT INDIVIDUAL WORK METHODOLOGY: PRACTICAL LEARNING BASED ON CASES AND PROBLEMS, AND EXERCISE RESOLUTION INDIVIDUAL AND GROUP OR COOPERATIVE WORK WITH THE OPTION OF ORAL OR WRITTEN PRESENTATION INDIVIDUAL AND GROUP TUTORIALS TO RESOLVE DOUBTS AND QUERIES ABOUT THE SUBJECT.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40




Extraordinary call: regulations

The course syllabus may change due academic events or other reasons.