PhD Studentship - Wearable Brain-Computer Interface for Treating Amyotrophic Lateral Sclerosis (ALS)

PhD Studentship - UCL Institute of Healthcare Engineering EPSRC PhD Studentships on Digital Health

Project Title:  

Wearable Brain-Computer Interface for Treating Amyotrophic Lateral Sclerosis (ALS) With A Neural Replacement Strategy

Project Supervisor:

Dr Dai Jiang | Dept Electronic and Electrical Engineering | UCL

Project Co-Supervisors:

Dr Barney Bryson / UCL Queen Square Institute of Neurology | UCL

Professor Andreas Demosthenous / Dept Electronic and Electrical Engineering | UCL

Deadline: 10^th July 2022.

Project Description

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease of motor neurons leading to wasting, paralysis, and eventual death from respiratory failure within 3 to 5 years. There is projected 69% global increase in the number of ALS cases worldwide by 2040, and 30% increase in UK cases. The average direct medical cost for an ALS patient is USD $3,436 per month, hence the increase will add at least an additional USD $95 million to the current healthcare costs in the UK. There is currently no cure for ALS.

The UCL Dept Electronic and Electrical Engineering and UCL Institute of Neurology have been developing a novel neural replacement strategy for ALS treatment that is capable of restoring functional control of paralysed muscles. This advanced therapy engrafts stem cell-derived motor neurons into peripheral motor nerves that supply specific target muscles, combined with a fully implantable optogenetic stimulator performing controlled, highly-selective optical stimulation, to restore a wide range of motor functions to ALS patients.

As part of the ongoing efforts to develop a combined therapy to restore essential activities of daily life (ADL) in ALS patients, this PhD study aims to develop a non-invasive, miniaturised brain-computer interface (BCI) that is wirelessly coupled to the implantable optogenetic stimulator to restore voluntarily controlled movement. The study will investigate three major research questions:

-  Can the intended movement for ADL, such as reaching and grasping, be accurately detected and classified in real-time from brain activities using non-invasive electroencephalogram (EEG)?

-  Can the EEG detection and classification function be implemented with satisfactory accuracy and latency in miniaturised electronic circuits, which is both power and area-efficient, and with optimal number of electrodes/channels, suitable for daily-life use?

-  Can the BCI be wirelessly integrated with the implantable multi-channel optogenetic stimulator, where optimal stimulation strategies can be developed to facilitate coordinated muscle contraction to achieve the recognised movement intention?

and the PhD investigation will be conducted in three stages:

1) Stage 1: Developing algorithms for recognising EEG motor activity, and integrating EMG feedback for closed-loop optical stimulation;

2) Stage 2: Developing integrated multichannel EEG circuits with built-in machine-learning processor;

3) Stage 3: Developing a user interface and communication platform integrating the BCI with the implantable stimulator.

This project also involves collaboration with multiple departments within UCL, along with multiple charities and corporations. Interactions are also planned with other stakeholders notably the UCL Institute of Healthcare Engineering (IHE) (https://www.ucl.ac.uk/healthcare-engineering/ ).

Funding :

• 4 years fees (home rate);
• Maintenance stipend at the UCL EPSRC DTP enhanced rate (£19,062 in 2022/23, rises with inflation each year);
• Research Training Support Grant (RTSG) of £4,800. This is to cover additional costs of training e.g. courses, project costs, conferences, travel;

Studentships are automatically renewed each year provided that sufficient academic progress is made.

Qualifications required:

Candidates should have or expect to achieve an excellent degree(s) (BEng/MEng/MSc) in Electronic/Electrical Engineering, Computer Science or related disciplines. The ideal candidate would have experience/knowledge on one of the followings: 

• Hardware / Sensor Technology
• Machine Learning Technology
• Healthcare technology
• Python or related programming languages

The ideal candidate should also be passionate about healthcare, along with how technology can make a difference in healthcare. The ideal candidate will also have excellent communication skills in order to interact with researchers from various disciplines.

Eligibility:

Applicants must fulfil the academic entry requirements for the programme they are applying to. In addition, applicants must also fulfil eligibility criteria based on nationality / residency specified below:

Funding eligibility criteria based on nationality

• UK nationals are eligible provided they meet residency requirements.
• EU nationals with settled status are eligible.
• EU nationals with pre-settled status are eligible provided they meet residency requirements.
• Irish nationals living in UK or Ireland are eligible.
• Those who have indefinite leave to remain or enter are eligible.
• All others are classified as "International".

Residency requirements for UK nationals

• Living in EEA or Switzerland on 31-Dec-2020 (at that time UK was considered part of EEA) and lived in UK, EEA, Switzerland, or Gibraltar for at least 3 years immediately before the studentship begins.
• Lived continuously in UK, EEA, Switzerland, or Gibraltar between 31-Dec-2020 and the start of the studentship.

Residency requirements for EU, EEA, or Swiss nationals with pre-settled status

• Living in UK by 31-Dec-2020 (a requirement to receive pre-settled status).
• Living in UK, EEA, Switzerland, or Gibraltar for at least 3 years immediately before the studentship begins.

These studentships are open to those with Home and International fee status (including EU); however, the number of students with International fee status which can be recruited is capped according to the EPSRC terms and conditions so competition for International places is particularly strong. EPSRC terms and conditions.

How to apply:

Applications must be made using the UCL online application system and Applications should be made using the UCL postgraduate study application form. Please mark it to the attention of Dr Dai Jiang or Prof Andreas Demosthenous.

The application must be accompanied by a curriculum vitae (with publications if any); transcripts; a personal statement that includes how the candidate experience aligns with the proposed research; and two references.

The successful applicant is expected to start on 26-Sep-2022.

Contact:

For informal enquires please contact Dr. Dai Jiang ([Email Address Removed]) or or Prof Andreas Demosthenous ([Email Address Removed] ).

About UCL, the Department of Electronic and Electrical Engineering and the Queen Square Institute of Neurology

Further information regarding UCL may be found at: www.ucl.ac.uk/

Information about the departments may be found at: https://www.ucl.ac.uk/electronic-electrical-engineering and https://www.ucl.ac.uk/ion/