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

Knowledge and Understanding

Explaining 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.

Explaining different technologies for recording neuronal signals from the brain.

Intellectual Skills

Critical reading and discussing of recently published papers on brain-machine interfaces.

Evaluating algorithms for classification and regression to decode neuronal signals.

Practical Skills

Writing matlab code to perform basic analysis and decoding of neurophysiological data in MATLAB.

Transferable Skills

Group work on analysis of brain signals.

Module syllabus

Introduction to neuroscience and brain-machine interfaces (BMIs):Physiology and anatomy of neurons, cell membranes, ion channels, dendrites, and axons. Action potentials. Synapses, neurotransmitter, excitation and inhibition.? A walk through the brain introducing its major parts (brain stem, cerebellum, thalamus, hypothalamus, amygdala, hippocampus and cerebral cortex). Motivations for BMI development. Example applications of BMIs for paralyzed patients and for healthy subjects.

Brain-machine interfaces for movement control and communication: Functional anatomy and plasticity of the motor cortex. Tuning of single neuron firing rates to movement kinematics (e.g. movement direction). Population vector. Control of computer cursors and reaching and grasping prostheses using multiple single neuron activity in monkeys and humans. Neuronal adaptivity during BMI control. BMI training strategies for paralyzed patients. Alternative recording technologies: electrocorticography (ECoG), electro- and magnetoencephalography (EEG&MEG). EEG based BMIs using movement imagination of different parts of the body. BMI control via operant conditioning of EEG signals or single-neuron firing rates. BMIs based on the P300 signal. Rat navigation guided by remote control.

Theory of BMI decoders:Population vector. Linear discriminant analysis. Linear filter. Bayesian decoding. Kalman filter.

Electrodes for BMIs:Tungsten electrodes, microwire arrays, Utah arrays, tetrodes, Michigan probes, electrocorticography (ECoG) grids. Extracellular recording hardware and signal processing. Spike detection and spike sorting. The electrode-tissue interface: gliosis and scarring. Biocompatibility issues.

Read-out of non-motor functions from brain activity: Introduction to functional magnetic resonance imaging (fMRI). Lie detection. Decoding visual images seen by subjects. Decoding conscious visual perception and unconscious determinants of decisions.

Deep brain stimulation (DBS):Parkinson`s disease, tremor, dystonia and their treatment. Technology, mechanisms, parameters and limitations of DBS. Computational modelling of DBS.


 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.


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

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

●  Written report: BMI Competiton; 20% weighting; Counts 50%. Report about coursework.

Feedback : Feedback is given during the GTA sessions.