Brain-computer Interfaces (BCIs) provide a potential way for paralyzed people to communicate with the outside world and restore motor function that has been impaired by devastating neuromuscular disorders. In general, there are two major types of BCIs based on electroencephalography (EEG): non-independent BCIs and independent BCIs.
Regarding non-independent BCIs, steady state visual evoked potentials (SSVEPs) are particularly attractive due to high signal to noise ratio (SNR) and robustness. SSVEP is a resonance phenomenon that can be observed mainly in electrodes over the occipital and parietal lobes of the brain when a subject looks at a light source flickering at a constant frequency. In this case, there is an increase in the amplitude of the EEG at flickering frequencies and their harmonics and there are different methods to extract the frequency components of SSVEPs. The first task of this project aims to study SSVEP based BCI. The goal is to develop novel machine-learning algorithms to realize stable and accurate classifications for multiple human commands. Regarding independent BCIs, motor imagery (MI) is a very popular paradigm. The neurophysiological phenomenon called event-related desynchronization (ERD)/ synchronization (ERS) accompanying real and imagined body part movement lays the fundamental mechanism for classifying motor imagery based BCIs. Due to the inherent complexity of brain dynamics and the characteristics of ERD/ERS rhythm being highly dynamic and subject specific, classification accuracy is generally not high enough and varies dramatically amongst people. Hence, the widespread use of MI based BCI out of the laboratory is still impossible. The second task of this project aims to study MI based BCI. The goal is to develop novel machine-learning algorithms to realize stable and accurate recognition of human intentions.
This research project will be carried out as part of an interdisciplinary integrated PhD in the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI). The ART-AI CDT aims at producing interdisciplinary graduates who can act as leaders and innovators with the knowledge to make the right decisions on what is possible, what is desirable, and how AI can be ethically, safely and effectively deployed. This project is aligned with the topic focus of ART-AI in three key ways.
First, advanced machine learning methods and novel paradigms in AI will be explored to enhance the efficacy of BCI. For example, the new paradigms in [1, 2] may be adopted or further explored.
Second, in order to guarantee the safety of BCI systems, autonomous technologies in robotics will be introduced. Shared control between human and machine will be proposed, which may significantly reduce the risks of a pure BCI system. Multiple signals including EEG may be used to develop multimodal human-robot interactions, which may also improve the safety .
Thirdly, the ethical issues and verification of the BCI-based system will require research into how medical devices are regulated and how the designers should or should not be held accountable for the devices.
This project aims to realize practical BCI-assisted systems with high performance, which can enhance quality of life for disabled persons and the aging population. Advanced machine learning technologies will be developed to process the brain signals and improve the performance of traditional BCIs.
Applicants should hold, or expect to receive, a First or Upper Second Class Honours degree. A master’s level qualification would also be advantageous. Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.
Informal enquiries about the project should be directed to Dr Dingguo Zhang on email address [Email Address Removed].
Enquiries about the application process should be sent to [Email Address Removed].
Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP02&code2=0002
Start date: 28 September 2020.
ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum in 2019/20, increased annually in line with the GDP deflator) and a training support fee of £1,000 per annum.
We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.
1. Effects of Task Complexity on Motor Imagery Based Brain-Computer Interface, Mashat, M. E. M., Lin, C-T. & Zhang, D., 1 Oct 2019, In : IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27, 10, p. 2178-2185 8 p.
2. A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device Wang, M., Li, R., Zhang, R., Li, G. & Zhang, D., 10 Apr 2018, In : IEEE Access. 6, p. 26789-26798 10 p.
3. Human-to-human closed-loop control based on brain-to-brain interface and muscle-to-muscle interface, Mashat, M. E. M., Li, G. & Zhang, D., 1 Dec 2017, In : Scientific Reports. 7, 1, 11001.
4. Toward Multimodal Human-Robot Interaction to Enhance Active Participation of Users in Gait Rehabilitation, Gui, K., Liu, H. & Zhang, D., 1 Nov 2017, In : IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25, 11, p. 2054-2066 13 p., 7926461.
5. Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain, Li, G. & Zhang, D., 16 Mar 2016, In : PLoS ONE. 11, 3, p. e0150667
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