The course

Biomedical Electronic Engineering (MSc)

This MSc has a focus on biomedical engineering fundamentals to healthcare practice, ranging from biomedical engineering, to embedded electronic systems, to machine learning. Using open-source tools for biosensing and neuroscience, you will work in a creative space with the aim of making brain-computer interfacing and other biosensors more accessible for rapid prototyping and quicker delivery of products in the consumer landscape.

On completion of this masters you'll have an advanced understanding of:

– how to analyse and formulate a wide range of biomedical problems using signal processing, embedded electronic systems, and machine learning.

– how to design, create and integrate software and hardware components of modern biomedical engineering systems and computer-controlled equipment

– the processes involved in constructing, analysing and solving biomedical problems into healthcare solutions

– the healthcare context in which engineering is practiced, as well as the effects of engineering projects on society

You will have the opportunity to gain advanced, research-specific skills and academic knowledge to take forward into further study or into your career.
You will become part of a dynamic research environment where you will acquire industrially relevant knowledge to make a successful contribution to tomorrow’s systems that support everyday life.

From time to time, we make changes to our courses to improve the student and learning experience. If we make a significant change to your chosen course, we’ll let you know as soon as possible.

Course structure

Core Modules

This is a major group project in which students will work on an agreed practical problem that is relevant to tomorrow's societal needs and agreed with their supervisor. The working practice of the groups will be modelled on industrial practices in terms of planning, keeping proper records of meetings and the progress of work, and students will each take on a responsibility within the team that is vital to the professional and successful running of the group project. The overall aim is to provide students with a full appreciation of mechanisms that can support professional group working and its management in engineering practice in the context of exploring and researching solutions to a topic relevant to society.
The aim of this module is to provide students with advanced knowledge of voice sythesis, recognition and processing in the context of present-day and future engineering systems that make use of a voice input or output. The indicative content for this course includes the synthesis of human speech and singing in terms of the sound source and sound modifiers in practice to create electronic voice signals, standard voice processing techniques, used for example, to enhance speech quality, remove background noise and improve perceived voice quality, to design hearing aids and techniques used for automatic speech recognition, for example, Apple's 'Siri' system.
The aim of this module is to provide theoretical and practical knowledge relating to biomedical engineering, human physiological phenomena and the electronics used to acquire them safely and accurately. The indicative content for this module includes the study of fundamental modern healthcare technologies in relation to the data acquisition of EEG, ECG, EMG and PPG signals. The nature of these signals will be studied along with engineering techniques for developing diagnostic and therapeutic devices from them. Further, medical imaging technologies such as ultrasound, MRI and CT scanners will be studied. Signal processing and pattern recognition techniques relevant to biomedical engineering applications will also be investigated. Finally, students will undertake group projects over the last few weeks of term which will revolve around developing complete end-to-end bioinstrumentation systems.
The aim of this module is to provide theoretical and practical knowledge relating to pattern recognition. The indicative content for this module includes the study of fundamental pattern recognition in relation to supervised and unsupervised learning. Topics will include Bayesian decision theory, Artificial Neural Networks and Support Vector Machines (amongst others). The nature of these algorithms will be studied along with engineering techniques for developing smart applications. Further, deep learning for biomedical engineering applications (e.g. classification of electrocardiograms) will be visited. Finally, students will undertake a coursework to an apply an appropriate machine learning methodology to solve a real-world biomedical problem.
The module extends the knowledge acquired in digital systems with advanced topics in the emergent area of FPGA based system on chip design. The module will cover state-of-the-art features available in modern FPGAs exploring their fine-grained internal architecture and embedded macro blocks such as DSP slices, IPs and hardcore/softcore processors. A design language based on C/C++ will be presented as an alternative to traditional RTL design (VHDL). High level synthesis tools will be used to compute signal processing applications.
The aim of this module is to provide students with the opportunity to carry out an in-depth engineering project, potentially in collaboration with industry, to solve a real-world problem or create a novel product. For specialised MScs, the project will be related to the specialisation topic.

Optional Modules

All modules are core

Teaching & assessment

This MSc consists of eight modules and a dissertation. Teaching follows several different complementary models: face-to-face, online, pre-recorded, workshops, presentations, practical sessions, labs. Assessments cover a variety of activities: groupwork, presentations, reports, Moodle quizzes, etc. Across the four MScs, examples and case-studies are international and cover many different backgrounds. Modules feature built-in formative assessments (e.g. Moodle quizzes, workshops, presentations) that complement and lead up to summative assessment.

Students have a close relationship with their tutors, and with the teaching staff in general, which means they have many opportunities for feedback. They receive oral feedback in workshops, presentations, practical sessions, and labs.

Entry requirements

2:2

Electronics, Computer Systems Engineering, Biotechnology or Biomedical Engineering background

International & EU requirements

English language requirements

MSc Biomedical Electronic Engineering requires:

IELTS: 6.5 overall. No subscore lower than 5.5.
Pearson Test of English: 61 overall. No subscore lower than 51.
Trinity College London Integrated Skills in English (ISE): ISE III.
Cambridge English: Advanced (CAE) grade C.
TOEFL iBT: 88 overall, with Reading 18 Listening 17 Speaking 20 Writing 17.
Duolingo: 120 overall and no sub-score below 100.

Your future career

This specialised course provides a strong theoretical and practical understanding of biomedical electronic engineering with links to industry-related topics and employability skills.

Students on this course will have placement opportunities to gain real-world skills and experience that will enable you to connect knowledge to global health-related challenges.

Prospective career pathways include:

Biomedical Engineer
Electronic Engineer
Healthcare IT engineer
Project Engineers
Wearable Technology Consultant

Fees, funding & scholarships

Home (UK) students tuition fee per year*: £14,400

EU and international students tuition fee per year**: £26,100

Other essential costs***: There are no single associated costs greater than £50 per item on this course.

How do I pay for it? Find out more about funding options, including loans, grants, scholarships and bursaries.

* and ** These tuition fees apply to students enrolled on a full-time basis in the academic year 2025/26. Students studying on the standard part-time course structure over two years are charged 50% of the full-time applicable fee for each study year.

Royal Holloway reserves the right to increase all postgraduate tuition fees annually. Be aware that tuition fees can rise during your degree (if longer than one year’s duration), and that this also means that the overall cost of studying the course part-time will be slightly higher than studying it full-time in one year. The annual increase for continuing students who start their degree in 2025/26 will be 5%.  For further information, see the  fees and funding , and terms and conditions.

** This figure is the fee for EU and international students starting a degree in the academic year 2025/26. Find out more

*** These estimated costs relate to studying this particular degree at Royal Holloway during the 2025/26 academic year, and are included as a guide. Costs, such as accommodation, food, books and other learning materials and printing, have not been included.