Brain Computer Interface (BCI) (Brunner et al, 2015) offers interaction and communication using thought processes without the need for explicit physical manipulation, giving rise to a powerful assistive technology. Within the BCI research community there have been significant technical advancements in terms of the signal processing, electrodes, and applications. However, a truly robust BCI is still elusive and techniques used need to be matched and tailored to the user. Zander & Jatzev (2012) highlight the differences in environment between the BCI computer laboratory, clinical and home settings for BCI use and point to a context aware system as a possible solution.
The following issues may influence operation of the BCI:
1. Understanding the environment through IoT sensing to attain better BCI control of smart devices, e.g. using proximity together with a BCI command to actuate a device. For example, ‘dim the nearest light bulb’, ‘close the curtains’.
2. Building an ontology that reflects IoT environment and BCI user state. The ontology can share understanding of the structure of information among people or software agents (Protégé).
3. Sharing autonomy between the user of the BCI and smart devices to determine the context of a task and hence provide improved actuation (Coogan & He, 2018, Zhang et al, 2019).
4. Using passive (affective) BCI components to provide context for human performance: How the user is feeling or how long they have been using the BCI? (Zander & Kothe, 2011).
Robustness and fitness for purpose degrades over time. With online adaptation on-going parameters extracted from the EEG and the session are used to provide updates to the classifier. Such systems may be able to respond to some transient and temporal conditions within the EEG and this calibration may quickly become outdated due external and physical factors. The objective of this PhD is to determine and use factors, input modalities and smart devices to help in the continual re-adjustment of the BCI system to best meet the tasks, needs and characteristics of the user.
Clemens Brunner, Niels Birbaumer, Benjamin Blankertz et al (2015) BNCI Horizon 2020: towards a roadmap for the BCI community, Brain-Computer Interfaces, 2:1, 1-10C.
G. Coogan and B. He, "Brain-Computer Interface Control in a Virtual Reality Environment and Applications for the Internet of Things," in IEEE Access, vol. 6, pp. 10840-10849Protégé, https://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html
Zander T.O., Kothe C. Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. J Neural Eng, 8:025005, 2011
Zander, T.O., Jatzev, S. & (2012). Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment. J Neural Eng. 2012 Feb;9(1):016003. https://www.ncbi.nlm.nih.gov/pubmed/22156069X.
Zhang, L. Yao, S. Zhang, S. Kanhere, M. Sheng and Y. Liu, "Internet of Things Meets Brain–Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity," in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2084-2092, April 2019.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Friday 7 February 2020
Late March 2020
The largest of Ulster's campuses
Monday 2 December 2019
Subject: Computer Science and Informatics
When applying for this PhD opportunity please quote reference number: