Classification and Segmentation of fMRI Spatio-temporal Brain Data With a Neucube Evolving Spiking Neural Network Model
Doborjeh, MG; Capecci, E; Kasabov, N
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The proposed feasibility analysis introduces a new methodology for modelling and understanding functional Magnetic Resonance Image (fMRI) data recorded during human cognitive activity. This constitutes a type of Spatio-Temporal Brain Data (STBD) measured according to neurons spatial location inside the brain and their signals oscillating over the mental activity period ; thus, it is challenging to analyse and model dynamically. This paper addresses the problem by means of a novel Spiking Neural Networks (SNN) architecture, called NeuCube . After the NeuCube is trained with the fMRI samples, the 'hidden' spatio-temporal relationship between data is learnt. Different cognitive states of the brain are activated while a subject is reading different sentences in terms of their polarity (affirmative and negative sentences). These are visualised via the SNN cube (SNNc) and then recognized through its classifier. The excellent classification accuracy of 90% proves the NeuCube potential in capturing the fMRI data information and classifying it correctly. The significant improvement in accuracy is demonstrated as compared with some already published results  on the same data sets and traditional machine learning methods. Future works is based on the proposed NeuCube model are also discussed in this paper.