# Biomedical Engineering (MSc)

## Brain Machine Interfaces (PG)

### Module aims

This course introduces a technology that is no longer the domain of science fiction, but has become science and is partially used already in clinical settings: the interfacing of the human brain to electronic circuitry. The course will emphasize currently developed and used technologies such as brain-machine interfaces (e.g. for the restoration of movement and communication capabilities of paralyzed patients) and deep-brain stimulation (such as for treatment of Parkinson`s disease).

### Learning outcomes

Explain different brain-machine interfaces, e.g. for movement control and communication, for reading-out non-motor functions from brain activity or for deep brain stimulation Explain different technologies for recording neuronal signals from the brain Evaluate algorithms for classification and regression to decode neuronal signals Write Matlab code to perform basic analysis and decoding of neurophysiological data in MATLAB Work in a group to analyse brain signals

### Module syllabus

Introduction to neuroscience and brain-machine interfaces (BMIs) Brain-machine interfaces for movement control and communication Theory of BMI decoders Electrodes for BMIs Read-out of non-motor functions from brain activity Deep brain stimulation (DBS)

### Pre-requisites

Basic linear algebra: vector operations (vector sum, scalar product etc.), matrix and matrix vector multiplication, matrix inversion Basic calculus: differentiation, integration, difference equations, differential equations Basics of probability theory: random variables, probabilities, probability distributions, joint, marginal and conditional probabilities, Gaussian distribution, stochastic processes Basics of matlab programming: variables, conditional expressions (e.g. if-then-else), loops (e.g. for, while), functions, basic mathematical expressions (e.g. vector and matrix operations, trigonometric functions), basic graphics

### Teaching methods

Students will be taught over one term using a combination of lectures and practical labs. Lecture sessions will be made available on Panopto for review and supplemented with technologies to promote active engagement during the lecture such as 'learning catalytics'. Labs will be based on taught content from lectures to reinforce these topics and allow students to test their understanding.

### Assessments

The module will be assessed by the submission of one written report and a final exam in the summer term.

Examinations:
●  Written exam: ; 50% weighting
Rubrics: 1.5 hour, 3 questions. Partially multiple choice question exams.
Outline answers to past papers will be available

Courseworks:
●  Written report: BMI Competiton; Counts 50%. Report about coursework.

Feedback : Feedback is given during the GTA sessions.