Neuromorphic Neural Network Parallelization on CUDA Compatible GPU for EEG Signal Classification

The purpose of the project described in this paper is to implement a Spiking Neural Network, on a CUDA driven Nvidia video-card, which can learn predefined samples on images presented as input data. With experimental EEG signals pre-processed using the Wavelet transform into an image set, it can learn to classify inputs into a certain category by applying a proprietary algorithm, presented in the paper. The implementation of the spiking neural network is done in CUDA C, with the use of the card's inner GPU. The GPU has the functionality to parallelize multiple tasks, which can enable the neural network to do fast calculations even with large amounts of data. The application can be controlled with a GUI, in which the user can modify the base parameters of the system, make tests, or it can train the system. Performance results are given in terms of computation speed and classification accuracy.