- A/Professor James Roberts, QIMR Berghofer
- Dr Kartik Iyer, QIMR Berghofer
Aims and Methodology
Source reconstruction of electroencephalography (EEG) in adults has become a widely used technique to derive estimated electrophysiological signal representations occurring at specific anatomical locations. Yet there remains a dearth of source-based EEG techniques in paediatric populations which feasibly and accurately capture the same representations. The availability of a streamlined source based EEG framework would provide an essential platform for neuroscientists in the paediatric research space to analyse their EEG data with respect to neuroanatomy.
To assess the feasibility of current brain mapping techniques that reconstruct individual scalp-EEG sources to underlying neuroanatomical sources across a paediatric population.
Source reconstruction of EEG in children across several age groups can be feasibly achieved to provide an accurate representation of scalp-derived EEG to anatomy.
The student will first commence a review of current methods and tools used to reconstruct EEG to anatomy, and examine the most efficient framework to which child-based EEG can be processed. In liaison with their supervisor, they will then examine the most viable pathway towards generating accurate paediatric head models for EEG across various age groups in children and adolescents.
The student will have access to two large existing EEG datasets:
- The Healthy Brain Network dataset, which contains EEG data (128 channels) in over 1800 children across 5 to 21 years of age, and
- a normative, clinically acquired EEG dataset (18 channels) with 800 children aged between 0 to 16 years of age. Whilst it is certainly not expected that the student will assess all of this data, they will utilise a subset of age representative samples, as guided by their supervisors, to assess source reconstruction methods.
Location of Research
QIMR Berghofer (Herston)
- Literature search and review
- data collection
- data analysis.
Students gain skills in literature review, data analysis and computational skills with respect to EEG processing. They will have an opportunity to present their findings to our research group at regularly held lab meetings. If successful, the student will be offered an opportunity to contribute to any publication(s) arising from the project.
Skills and experience
Familiarity with neuroanatomy and neurophysiology would be highly desirable. Whilst not essential, some familiarity with statistical programming software such as Matlab, R and/or Python packages would be desirable.
Resources / Access
Supervision and training, desk space, and computer.
Contact the supervisor for more information: email@example.com
A/Professor James Roberts is the principal supervisor.