
Overview
Background
Having done a Newton Fellowship at MRC Cognition and Brain Sciences Unit, The University of Cambridge, Dr Hamid Karimi-Rouzbahani is now an ARC DECRA fellow at The University of Queensland.
His interests are at the intersection of Computational, Cognitive and Clinical Neuroscience and combine neural signal processing (e.g., EEG, MEG and fMRI), machine learning (e.g., deep neural networks) and mathematical modelling.
His computational work involve the development of multidimensional connectivity and decoding analysis methods to study information coding and transfer across the brain. His cognitive interests include research into the neural bases of visual perception, attention and the multiple-demand system. His clinical work develops methods to quantify and localise brain areas involved in epilepsy.
Availability
- Dr Hamid Karimi-Rouzbahani is:
- Available for supervision
Works
Search Professor Hamid Karimi-Rouzbahani’s works on UQ eSpace
2011
Journal Article
Diagnosis of Parkinson's disease in human using voice signals
Rouzbahani, Hamid Karimi and Daliri, Mohammad Reza (2011). Diagnosis of Parkinson's disease in human using voice signals. Basic and Clinical Neuroscience, 2 (3), 12-20.
Funding
Current funding
Supervision
Availability
- Dr Hamid Karimi-Rouzbahani is:
- Available for supervision
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Available projects
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Developing novel information decoding and tracking methods to study brain and cognition
The Brain is one of the most complicated information processing systems known. However, we have not yet fully discovered how the brain processes information and solves complicated cognitive problems. This project is aimed at enhancing state-of-the-art methodologies in neural data analysis. While great progress has been made in the past decades on developing methods for neural data analysis, the development of knowledge now allows us to develop methods which can provide unprecedented insights into the brain. This project works on two aspects of neural information processing including how neural activations reflect meaningful information and how those activations transfer information from one area of the brain to another indifferent tasks.
This project involves programming in different programming languages including PYTHON and MATLAB and analysing different modalities of neural data including electroencephalography (EEG), magnetoencephalography (MEG), functional Magnetic Resonance Imaging (fMRI), neurophysiology data and calcium imaging. These datasets will be collected either in the lab by the PhD student and/or obtained from publicly available sources. The project also uses stimulation devices such as Transcranial Magnetic Stimulation (TMS) to evaluate causal role of interference on human cognition.
Supervision history
Current supervision
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Doctor Philosophy
Developing novel information decoding and tracking methods to study brain and cognition
Principal Advisor
Other advisors: Professor Jason Mattingley
Media
Enquiries
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