Personalised modelling on integrated clinical and EEG Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network system

This paper introduces a novel personalised modelling framework and system for analysing Spatio-Temporal Brain Data (STBD) along with person clinical static data. For every individual, based on selected subset of similar to this individual clinical data, a subset of STBD is used for training a personalised Spiking Neural Network (PSNN) model using the recently proposed NeuCube SNN architecture. The proposed method is illustrated on a case study of personalised modelling using clinical and EEG data of two groups of subjects - drug addicts and addicts under medication. The PSNN models help to achieve a better classification accuracy compared to global SNN models or when using traditional AI methods. A PSNN model visualisation enables discovery of new knowledge about individual persons and to distinguish complex STBD across subjects.