Classification and Regression of Electroencephalography (EEG) Data (BAGNALLA_U22SCIO) at University of East Anglia on FindAPhD.com

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Prof T Bagnall, Dr S Sami No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

Electroencephalography (EEG) records electrical activity in the brain using a series electrodes placed on the scalp. EEG equipment is relatively cheap and portable and is currently one of the most widely used non-invasive brain imaging tools in neuroscience and in the clinic. In research, EEG recordings are used in a wide range of fields, including medicine (e.g. diagnosis of epilepsy or the early detection of dementia), computer science (e.g. brain computer interfacing ( BCI) and human activity recognition (HAR)) and psychology (e.g. the study of cognitive development). Each field has a range of related tasks and experimental structures, and each has a different default methodology. However, at the heart of many EEG related research questions is the problem of building a predictive regression or classification problem. This can be diagnostic (does the EEG recording of a patient indicate they have dementia?), descriptive (can we tell from the EEG recording whether an individual is moving their left or right arm?), or cognitive (is the subject looking at a picture of a face or random noise?). This project will focus on developing the latest innovations in times series classification [1,2] (TSC) to the general problem of learning from EEG. We will adapt the latest version of the HIVE-COTE algorithm [3] for EEG classification through embedding spatial information in the transformation process, and collaborate with experts in the field [4] to compare our novel approaches against accepted gold standard methodologies. We will collate publicly available datasets, develop solutions within an open source framework [5] and conduct our own experiments to try and answer the core question of this project: can we automated the process of building predictive models from EEG signals in a way that is as good as, or better than, hand crafted solutions?

For more information on the supervisor for this project, please visit the UEA website www.uea.ac.uk

The start date is 1 October 2022

Entry requirements: Acceptable first degree 2:1 Computer science or related fields, neurosciences

Funding Notes

This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend of £15,609 for 3 years. Applicants who are not eligible for home tuition fees are welcome to apply but they will be required to fund the difference between home and international tuition fees (which for 2021-22 are detailed on the University’s fees pages at https://www.uea.ac.uk/about/university-information/finance-and-procurement/finance-information-for-students/tuition-fees. Please note tuition fees are subject to an annual increase).

References

[1] Bagnall, A., Lines, J., Bostrom, A., Large, J. and Keogh, E. The Great Time Series Classification Bake Off: a Review and Experimental Evaluation of Recent Algorithmic Advances. Data Mining and Knowledge Discovery, 31(3): 606-660, 2017
[2] Lines, J., Taylor, S. and Bagnall, A Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-based Ensembles. ACM Transactions on Knowledge Discovery from Data. 12(5): 52-87, 2018.
[3] Middlehurst, M., Large, J., Flynn, M., Lines, J., Bostrom, A., Bagnall, A. HIVE-COTE 2.0: a new meta ensemble for time series classification. Machine Learning, in press, 2021.
[4] Sami S, Hughes LE, Williams N, Cope T, Henson R, Rowe JB Neurophysiological signatures of Alzheimer’s disease and Frontotemporal lobar degeneration: pathology versus phenotype Brain. 2018;141(8):2500-2510.
[5] Löning, M. Bagnall, A., Ganesh, S., Kazakov, V., Lines, J and Király, F. sktime: A Unified Interface for Machine Learning with Time Series, Workshop on ML for Systems at NeurIPS, 2019.

Where will I study?

University of East Anglia

We’re one of the UK’s leading research institutions, ranked 10th in the UK for the quality of our research outputs and with more than 1,500 research students.

Classification and Regression of Electroencephalography (EEG) Data (BAGNALLA_U22SCIO)

University of East Anglia School of Computing Sciences