The Brain Computer Interface (BCI) offers interaction and communication using thought processes without the need for explicit physical manipulation, potentially 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. However, there are many factors that can quickly render the tailored system to be less than optimal. Zander and Jatzev (2012) highlight the differences in environment between the laboratory, clinical and home setting for BCI use and point to a context aware system as a possible solution to the transient and temporal operating conditions.
They categorise 3 layers of abstraction of the states within a BCI system:
1. the status that is external and easy to observe. 2. relates to factors within the human brain including covert cognitive state. BCI feature space. This PhD will investigate how each of these states may influence the BCI: 1.Passive BCI components / affective components / performance: How the user is feeling, or how long they have been using the BCI.
2.External factors such as environmental variants can also impact performance. Connecting in with smart devices to report measures of environment or the context of a task.
3.Through understanding the environment through sensing, attain better BCI control of smart devices, e.g. using proximity and BCI command to actuate a device. In general, robustness and fitness for purpose degrades over time. With online adaptation on-going parameters extracted from the EEG and the session is used to provide updates to the classifier.
Such systems may be able to respond to some transient and temporal conditions within the EEG. A great effort is involved in choosing the optimum parameters for BCI systems and yet this calibration may quickly become outdated due external and physical factors. The ability to continually update the BCI parameters and indeed perform some level of remote monitoring of the system’s performance provides a greater opportunity for offsite technical support, a necessity for widespread home use.
The objective of this PhD is to determine and use factors, complementary input modalities (hybrid BCI) and smart devices to help in the continual re-adjustment of the BCI system (as a whole) to best meet the tasks, needs and characteristics of the user.
Allison, B.Z. (2011). Future BNCI: A Roadmap for Future Directions in Brain / Neuronal Computer Interaction Research. [Online] http://future-bnci.org/images/stories/Future_BNCI_Roadmap.pdf [Accessed: September 2012].
Spüler, M., Rosenstiel, W., & Bogdan, M. (2012a). Online adaptation of a c-VEP brain-computer interface (BCI) based on error-related potentials and unsupervised learning. PloS one, 7(12), e51077.
Zander, T.O., Kothe, C., Jatzev, S. & Gaertner, M. (2010). Enhancing Human-Computer Interaction with input from active and passive Brain-Computer Interfaces. In: Tan, D.S. and Nijholt, A. (eds.) Brain-Computer Interfaces, 181—199. Springer, London
Vice Chancellors Research Scholarships (VCRS)
The scholarships will cover tuition fees and a maintenance award of £14,777 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided). For Non EU nationals the candidate must be "settled" in the UK.
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Monday 18 February 2019
25 to 29 March 2019
The largest of Ulster's campuses
When applying for this PhD opportunity please quote reference number: