Personalised Modelling on STBD and Static Data


The Brain-Like Artificial Intelligence (BLAI) is pioneered by Prof.Nikola Kasabov and here it is applied to a specific application.

The developed NeuCube personalised modelling system integrates both static data (e.g. patient records; spatial attributes) and dynamic, spatio-temporal brain data (STBD). The system can be used for various R&D projects and also for clinical studies across cognitive and brain disease projects.

The system applies the following steps to process the static and dynamic data:

  1. Clustering of integrated static-dynamic data is performed using the new algorithm dWWKNN (dynamic weighted–weighted distance K-nearest neighbors). For a new individual xi, we rank the contribution of each of the k neighbouring samples based on integrated static-dynamic distance to the xi, giving greater rank to closer neighbors.
  2. Select the STBD subset belongs to the samples fell in the dWWKNN.
  3. Using the selected STBD for unsupervised learning in the NeuCube personalised SNN (PSNN) model.
  4. Test the model with STBD of  xi, which is unknown to the model.

Figure. The NeuCube-based personalised modelling for integrated static and dynamic data.

Related Papers,  Patents and Benchmarking

The proposed methods and systems, when compared with traditional statistical and machine learning methods, showed superior results in the following aspects:

  1. Better data analysis and classification/regression accuracy (by 10 to 40%);
  2. Better visualisation of the created models, with a possible use of VR;
  3. Better understanding of the data and the processes that are measured;
  4. Enabling new information and knowledge discovery through meaningful interpretation of the models.

See also some of the related papers and patents:

Doborjeh, M. G., & Kasabov, N. (2016, July). Personalised modelling on integrated clinical and EEG spatio-temporal brain data in the NeuCube spiking neural network system. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 1373-1378). IEEE.

N.Kasabov, V.Feigin, Z.Hou, Y.Chen, Improved method and system for predicting outcomes based on spatio/spectro-temporal data, PCT patent WO2015/030606 A2, US2016/0210552 A1. Granted/Publication date: 21 July 2016.

N.Kasabov, Data Analysis and Predictive Systems and Related Methodologies, US patent 9,002,682 B2, 7 April 2015.

R&D System

For this project, an R&D system has been developed based on NeuCube. The system can be obtained subject to licensing agreement.


The developer of this project is:

Maryam Gholami Doborjeh