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Research Thesis Topic

Detection and Prediction of Abnormalities for Diagnosis of Brain Diseases from Brain Signal Data

Topic ID:

Thesis Topic/Title:
Detection and Prediction of Abnormalities for Diagnosis of Brain Diseases from Brain Signal Data


Detection and prediction of abnormalities from brain signal data is a significant research area in modern medical technology. ‘Brain signal data’ refers to electroencephalogram (EEG) signal data. Currently, EEG is the most frequently-used technique for studying the functional states of the brain. EEG is becoming increasingly important in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. The study of the brain electrical activity, through the EEG records, is one of the most important tools for the diagnoses of brain diseases, such as epilepsy, brain tumour, head injury, sleep disorder, dementia and monitoring depth of anaesthesia during surgery. It may also be recommended for the treatment of abnormalities, behavioural disturbances (e.g. Autism), attention disorders, learning problems, language delay etc. If a brain abnormality can be accurately identified, patients can be given timely treatment to slow progression or avoid sudden deterioration. Furthermore, if impending abnormalities can be predicted in their early stages, the treatments can significantly improve patient’s’ survival and quality of life. Vast amounts of multi-channel EEG signals are visually analysed by neurologists to identify and understand abnormalities within the brain and how they propagate. Visual inspection of EEG signals is not a satisfactory procedure because there are no standard criteria for the assessment and it is time-consuming, error-prone, and subject to fatigue. Therefore, there is an ever-increasing requirement for developing an automatic analysis system to ensure the proper evaluation and treatment of brain disorder diseases. The aim of this project is to develop reliable, robust and analysis techniques that will be able to detect abnormalities from EEG signal data discovering brain disease. This project also helps to predict future abnormalities based on EEG signals. This project will advance the existing techniques in medical applications to identify brain disorder diseases and also to provide appropriate early-warning indicative information to reduce health risks and enhance health monitoring.

Principal Supervisor

Associate Supervisors

Research Affiliations
  • School of Agricultural and Environmental Sciences

Field of Research
  • Artificial Intelligence and Image Processing
  • Biomedical Engineering
  • Medical Biotechnology

Available Academic Programs
  • Doctor of Philosophy (DPHD)

Application Open Date

Application Close Date

USQ Scholarship Applications

Pre-approved for Ethics
Not Applicable

Admission Requirements

Please review the admission requirements for the academic program associated with this Thesis Topic

External Assoc supervisor from Centre for Applied Informatics, Victoria University, Melbourne.

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