INTELLECTE

We will test the hypothesis that a NeuCube-based methodology is more accurate than existing methods for EEG- or fMRI based BCI. This will be tested on neurorehabilitation and brain state recognition tasks. The team includes D.Taylor, N.Kasabov, Shepherd, E.Gaeta, R.Jones, S.Weddel, I.Khan and PhD students Capecci, Scott, Sengupta, Gholami, Chamberlain. Initial experiments have already shown good results. Data will be collected in the EEG lab of the FHES. Successful models will be implemented after 2014 on the robotic system of Z.Hou as part of the China Strategic Alliance Agreement, with a commercialisation prospect.

The first part of the project will test if a NeuCube-based approach will be suitable for modelling and understanding of human motion data. Here the team includes Taylor, Kasabov, McNair, Naude, Signal, PhD students Capecci and Scott. Data will be collected in the GATE lab of the FHES. If successful, results can be expected to be used after 2014 in clinical practice and for sport performance evaluation at AUT. A second part of the project will evaluate the suitability of the NeuCube approach for data-intensive and long–term patient monitoring with the involvement of Lowe, Hosseini, Parry, Harrison (U Otago) and students Baig and Hamano.

This project will apply the provisional patent on personalised modelling developed at KEDRI, on temporal or spatio-temporal data and will include case studies on stroke, diabetes and CVD. In its first part, this project will further prove the hypothesis that a NeuCube-based model is more accurate than existing methods for early prediction of stroke occurrence. The team on stroke experiments includes V.Feigin, R.Krishnamurti, N.Kasabov, Z.Hou, postdoc, PhD students M.Othman and V.Breen. Test data is already available for NZ and new data for China will be trialled next year. The project will also explore personalised modelling for risk prediction of diabetes and its CVD implications. This part will involve  A.Lowe, Al-Jumaily, Murphy and Plank (University of Auckland), Orr and Beban (ADHB).

This project will test the hypothesis that a NeuCube-based methodology is more accurate than existing methods for the prediction of response to treatment in the health sector. It will be tested on predicting the response to Methadone of people with addiction based on EEG data. The team includes G. Wang, N.Kasabov, R.Kydd, B.Russel, a postdoc and PhD students E.Capecci and M. Gholami. Test data has already been collected.

This project will explore the KEDRI patented method for personalised modelling on large and heterogeneous data sets that include both genetic and clinical information. The team, that includes C.Higgins, N.Kasabov, D.Love, D.Parry, and PhD V.Breen, will develop a generic tool and explore it on case study problems related to cancer and CVD diagnosis and prognosis. Data will be provided by Auckland Hospital (D. Love) and PEBL (U Otago).

This project will further test the hypothesis that a NeuCube-based methodology is more accurate than existing methods for the prediction of the establishment of ecological and environmental events. The first case study will be predicting the establishment of harmful species in NZ. NZ data has already been initially experimented and data from China will be trialled next as part of a tripartite project West China-East China–New Zealand. The second case study will be on the problem of early prediction of risk of earthquakes in NZ based on continuous stream of data collected through the GeoNet system. The team includes Kasabov, Pears, Worner, Yang, Ozawa, a postdoc and PhD students Hartono and Kristiani.

This project will develop and test a new method based on the NeuCube architecture for pattern recognition of Pulsars from fast and large streams of radio-astronomy data. If successful, a model implementation on a highly parallel neuromorphic hardware SpiNNaker is envisaged and a proposal to the SKA multibillion programme. The team includes S.Gulyaev, N.Kasabov, R.Pears, B.Pfahringa, G.Dobbie, postdoc and PhD students Scott, Hartono and Kristiani.

This project will test a methodology of using new methods including dynamic vision sensors (DVS) in combination with a neuromorphic classification system for the purpose of detecting fast moving objects and for face recognition based on video data. The project included W.Yan, R.Klette, D.Bailey, N.Kasabov, G.Indiveri and a PhD student Fahad Alvi.

The project will be a joint effort of Creative Technologies, Sentience Lab, and KEDRI in the development of a generic VR system for the visualisation and understanding of the 3D NeuCube connectivity and dynamics during and after training with spatio-temporal data. First steps have been already done on EEG brain data, but the system will be extended to be used when other types of complex data is being modelled (human motion; ecological and environmental; medical). The team includes Marks, Estevez, Kasabov, Taylor and student Hartono.

This project will explore the applicability of affective computing based on M.Sagar’s emotional baby model and of new wearable computing devices, such as the Goggle Glasses by M.Billinghurst, in the design of novel IIT. The project will be conducted by C.Walker, F.Joseph, M.Sagar, M.Billinghurst, N.Kasabov, a PhD co-supervised by creative technologies/KEDRI.

This project will explore several methods for complex system optimisation, including: evolutionary computation; quantum inspired optimisation; new types of smart meta-heuristic search algorithms. The methods will be tested on the problem of 3D printing design optimisation. The project will involve A.Connor, S.Singamneni, A.Roychoudhry (India), M.Zhang, N.Kasabov and PhD students F.Schmidt and B.Huang.

This project will integrate the work of all Themes and projects in a new, experimental IIT design approach. A framework will be developed for a collaborative work of several remotely located groups in the design of a distributed software/hardware system that will include elements of: data collection from many distributed sources including video and image data; machine learning of a neurocomputing classifier; system parameter optimisation; human-computer interaction; data flow and system operation visualisation. A case study specification will be decided during the INTELLECTE workshop in May. The team includes T.Clear, S.MacDonell, Z.Salcic, A.Malik, A.Connor, A/Prof.D.Damian (Canada), Da Zhang (PhD student) and the rest of the INTELLECTE team.