Real-world environments in today’s highly interconnected digital world are often non-stationary. Learning systems such as classifiers used for pattern recognition, make the stationarity assumption in input data distribution density during training and operation phases. Their field performance may become unsatisfactory and therefore such learning systems may have limited practical use. A very good example of a non-stationary environment is an magnetoencephalography and electroencephalography (MEEG) based brain-computer interface (BCI) in which a communication link is established between a computer and the user through a process involving thinkingimagining a set of cognitive tasks. In this, a classifier is used to detect the electrophysiological correlates, resulting from the cognitive tasks performed by the user [1-4]. The non-stationary MEEG characteristics has been one of the primary impediments in developing a practically useful BCI, despite decades of R&D work. In fact prevalence of such non-stationary systems is wide-spread, e.g., autonomous robots, share price trend predictions, spam filtering, surveillance, and bioinformatics. In all these areas, a learning system devised under the assumption of input data distribution invariance results in suboptimal performance while the system is being used either as a classifier or a predictive model.
To account for EEG non-stationarities appearing as covariate shifts, we have recently developed a covariate shift detection and adaptation methodology using an Exponentially Weighted Moving Average (EWMA) modelling strategy and successfully applied in a motor imagery based BCI [2-3]. Advancing this work further, this project aims to investigate how characteristics of non-stationary systems be effectively learned on-line in the absence of manual labelling of the streaming data. It will involve investigating a range of approaches applied in multiple applications in a collaborative way. It may involve developing techniques for learning from the changes in inputs alone, possibly based on transfer learning using archived data ; devising adaptation strategies that can make effective use of the available information to extract knowledge about the system variability and also making continuous update to account for the new information without forgetting already learnt information possibly using prediction error minimisation (PEM) based active inferencing and Bayesian belief propagation approach .
1.Youssofzadeh, Zanotto, Wong-Lin, Agrawal, Prasad, Agrawal (2016). Directed Functional Connectivity in Fronto-Centroparietal Circuit Correlates with Motor Adaptation in Gait Training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(11), https:doi.org10.1109TNSRE.2016.2551642
2. Chowdhary, Raza, Meena, Dutta & Prasad (2017). Online Covariate Shift Detection based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation. IEEE Transactions on Cognitive and Developmental Systems, 10(4), DOI: 10.1109TCDS.2017.2787040.
3.Raza, Prasad, & Li, (2015). EWMA Model based Shift-Detection Methods for Detecting Covariate Shifts in Non-Stationary Environments. Pattern Recognition, 48 (3). pp. 659-669.
4. Gaur, McCreadie, Pachori, Wang, & Prasad (2019). Tangent space features-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https:doi.org10.1142S0129065719500254
5. Friston, FitzGerald, Rigoli, Schwartenbeck & Pezzulo (2017). Active inference: A process theory. Neural Computation, 29(1):1–49, 2017.
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 support 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 support 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 support 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 support 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
23 to 24 March 2020
A key player in the economy of the north west
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