New technique for studying human brain activity
Summary
This project investigates stimulus reconstruction from surface EEG neural activity. Improvements to surface EEG recordings may proceed with the development of skull impedance modelling using impedance tomography and magnetic resonance imaging technology. We propose to investigate the influence of behaviour on stimulus reconstruction from EEG neural activity in primary auditory cortex.
Supervisor(s)
Associate Professor Craig Jin, Professor Philip Leong, Professor Alistair McEwan
Research Location
Electrical and Information Engineering
Program Type
Masters/PHD
Synopsis
This leading research project explores improvements to surface EEG imaging using impedance tomography and magnetic resonance imaging. Stimulus reconstructions from neural EEG activity provide a means to investigate primary auditory cortex.
Additional Information
Successful candidates likely have a background in electrical engineering, computational neuroscience, or biomedical engineering.
http://www.ee.usyd.edu.au/carlab
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Keywords
computational neuroscience, Brain imaging, EEGs, auditory neuroscience, stimulus reconstruction from neural activity, biomedical engineering, impedance tomography.
Opportunity ID
The opportunity ID for this research opportunity is: 1356
Other opportunities with Associate Professor Craig Jin
- Pattern analysis techniques for sound synthesis
- Interpolation of binaural impulse responses for virtual auditory displays
- Sound field recording and recreation
- Beamforming with acoustic vector sensors - Multiple acoustic source localisation using acoustic vector sensor arrays
- Speech separation and localisation using particle filtering
- Mapping 2D Images to 3D Shape
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Statistical models of ear shape and ear acoustics
- Binaural signal processing algorithms for hearing aids
- Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
- Impedance tomography for cardiac imaging: high speed tomography
- Medical diagnostics for neonates in the developing world
- FPGA-based Low Latency Trading
- Floating Point FPGA Architectures
- Placement-aware Hardware Description Languages
- Scalable vision machines
- Modelling Parkinson's disease using control models
- Novel Electrodes for rapid electrophysiological recording
- FPGA-based low latency machine learning
Other opportunities with Professor Philip Leong
- other research opportunities available at Faculty of Engineering
- FPGA-based Low Latency Trading
- Floating Point FPGA Architectures
- Placement-aware Hardware Description Languages
- Domain-specific Languages for GPU-based Computing
- Scalable vision machines
- Modelling Parkinson's disease using control models
- FPGA-based low latency machine learning
- FPGA control of photonic systems
- Test
- Mapping 2D Images to 3D Shape
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Statistical models of ear shape and ear acoustics
- Medical diagnostics for neonates in the developing world
- Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
- Impedance tomography for cardiac imaging: high speed tomography
- Novel Electrodes for rapid electrophysiological recording
- Binaural signal processing algorithms for hearing aids
Other opportunities with Professor Alistair McEwan
- Medical diagnostics for neonates in the developing world
- Electrical Impedance Tomography for stroke, biophysical monitoring and medical device design
- Impedance tomography for cardiac imaging: high speed tomography
- Novel Electrodes for rapid electrophysiological recording
- Implant electrode optimisation and neurolinguistics
- Subdivided electrodes to improve defibrillators and physiological measurements
- Electrical impedance modelling for implants
- Magnetic resonance electrical impedance tomography
- Development of a microwave catheter for cardiac ablation to treat ventricular tachycardia
- Resuscitation monitors for the neonatal intensive care uni (NICU)
- ARTIFICIAL INTELLIGENCE/MACHINE LEARNING ASSISTED NEWBORN CRITICAL CARE (VIRTUAL CRITICAL CARE MODELING)
- Mapping 2D Images to 3D Shape
- Next Generation Audio Coding
- Spherical multi-modal scene analysis
- Statistical models of ear shape and ear acoustics
- FPGA-based Low Latency Trading
- Floating Point FPGA Architectures
- Placement-aware Hardware Description Languages
- Scalable vision machines
- Modelling Parkinson's disease using control models
- Binaural signal processing algorithms for hearing aids
- FPGA-based low latency machine learning