On-line Learning, Classification and Interpretation of Brain Signals using 3D SNN and ESN

The paper proposes a novel hierarchical recurrent neural network architecture for on-line classification and interpretation of EEG data. It incorporates two dynamic pools of neurons - one based on NeuCube three dimensional structure of spiking neurons, spatially mapping a brain template and connected via spike-timing dependent plastic synapses and another Echo state neural network (ESN) reservoir of sparsely connected hyperbolic tangent neurons that is able to learn on-line to classify continuously extracted from the Cube spike-rate features. The aim of the work was to interpret and classify in a brain-inspired manner dynamic spatio-temporal brain signals. The achieved results demonstrate improved classification accuracy on a benchmark EEG data set along with a good interpretability of the data. In future, the proposed method can be used for classification of other brain spatio-temporal data, such as ECOG and fMRI.