KEDRI - Neurocomputing and Neuroinformatics

KEDRI
AUT

KEDRI
AUT Main Nav
Centre Banner
Main Content

Neurocomputing and Neuroinformatics

Current research projects:

  • Novel SNN Methods for Neurorehabilitation 
  • Integrated Brain Data Modelling (with Humboldt University-Germany)
  • Human Motion Modelling
  • Predicting Response to Treatment (e.g. Addiction; Schizophrenia)

A new design of a brain controlled neurorehabilitaion exoskeleton

Main objective will be to develop a working prototype of BCI device that will link the BCI with a functional electrical stimulation system. A secondary objective will be to look at the feasibility of using NeuCube to map neural activity during task performance. This will produce a powerful tool that will advance the understanding of cortical activation during task performance. Successful models will be used to design and implement in 2015 a new exoskeleton robotic system with Prof. Z.Hou as part of the China Strategic Alliance Agreement. 

Project Team

  • Prof. N. Kasabov
  • Prof. Z.-G. Hou (Chinese Academy of Sciences, China)
  • A/Prof. D. Taylor
  • Dr. D. Shepherd
  • Dr. H. Gaeta
  • Prof. R. Jones 
  • Dr. S. Weddell 
  • Dr. I. Khan 
  • J. Chamberlain
  • N. Scott
  • N. Sengupta
  • M. Gholami
  • E. Capecci

Brain Data Networks: methods and systems for computational modelling and analysis of multimodal, multiscale, spatio-temporal brain data

The human brain can be viewed as a dynamic, evolving information-processing system, probably the most complex one. Processing and analysis of information recorded from brain and nervous system activity, and modeling of perception, brain functions, and cognitive processes, aim at understanding the brain and creating brain-like intelligent systems. The main goal of this project is to bundle the diverse knowledge, know-how and technologies of scientists in Germany (Humboldt University) and New Zealand (KEDRI AUT) to create a new methodology and systems for efficient analysis and understanding of complex brain data, through synergistic research, using different data modalities. We plan to combine methods including neuroimaging techniques, novel signal processing methods and complex networks theories within the unifying computational framework called ‘NeuCube’. 

Project Team

  • Prof. N. Kasabov
  • A/Prof. D. Taylor
  • Dr. G. Wang
  • Dr. S. Marks
  • Prof. G. Ivanova (Humboldt University of Berlin, Germany)
  • Prof. J. Kurhs (Humboldt University of Berlin, Germany)
  • Prof. H.C. Hege (Humboldt University of Berlin, Germany)

Motion data analysis technology based on SNN

This project will continue testing if a NeuCube-based approach will be suitable for modelling and understanding of human motion data. Data has been collected in the GATE lab of the FHES. If successful, results can be expected to be used after 2015 in clinical practice and for sport performance evaluation at AUT.

Project Team

  • Prof. N. Kasabov
  • A/Prof. D. Taylor
  • Prof. P. McNair 
  • Y. Naude
  • N. Signal
  • N. Scott
  • E. Capecci

Predicting response to treatment of patients with schizophrenia

This project will apply the multimodal brain data integration method used in the project for "A holistic Framework Based on NeuCube for Multimodal Brain Data Modelling" to develop for the first time a NeuCube-based model for predicting the response to clozapine of patients with schizophrenia where full brain data for each patient will be included: EEG, fMRI, structural, DTI, genetic. The project will also continue previous work on developing a NeuCube-based method for more accurate prediction of response to methadone treatment of patients who are under opiates. Now the objective is to investigate for the first time ex-opiate users’ functional recovery on emotion processing and working memory associated with methadone treatment based on EEG data. Test data has already been collected. 

Project Team

  • Prof. N. Kasabov
  • A/Prof. D. Taylor
  • Dr. G. Wang
  • Prof. R. Kydd (The University of Auckland)
  • Dr. B. Russel (The University of Auckland)
  • C. McNabb (The University of Auckland)
  • N. Sengupta 
  • M. Gholami
  • E. Capecci


Last updated: 30 Oct 2014 6:16pm

Auckland University of Technology, New Zealand | Copyright © | Privacy | Site map