Project descriptionUnderstanding the processes of the human body is of paramount importance for recognising dysfunction. Trained medical staff currently undertake manual analysis of EEG recordings for diagnosing neurological conditions, with computer classification an active area of research. Both manual and automatic analysis of these signals could be improved with intelligent signal processing applied to the raw data in order to better highlight areas of interest. This project will take an existing adaptive bio-inspired model for enhancing objects in visual images and modify it to function on EEG signals. The hypothesis is the resulting signals will facilitate better classification of conditions.
Co-supervisorsA/Prof Kenneth Pope
Supervisors research focusMy goal is to bring robotics out of the lab and into the real-world. To stop having to adapt our environments to suit artificial systems and build artificial systems with the ability to adapt to different environments. I will continue to contribute to the technological and scientific progress of Australia both directly and by helping to guide future generations of entrepreneurs and engineering professionals. My research focus is biologically inspired sensors and signal processing. Data is everywhere; information is the lifeblood of the modern age, but the refinement of raw data into usable information is a complex task. Traditional approaches have tended to be linear and time-invariant, or at least deviate minimally from this paradigm. Adaptation is the hallmark of biological systems, which, by definition, is non-linear and time varying. By studying how biology works, and putting that processing into action, we can open up a new world of possibilities in many areas of technology.
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