Classification of EEG signals using spiking neural networks

In signal processing applications of conventional artificial neural networks, the processing time of the data is high and the accuracy rates are not good enough. At the same time, time-dependent processing is not possible. In this study, classification of EEG signals was performed using an artificial neural network including the characteristics of spiking neural networks. Successful results were obtained using large data sets. Moreover, by using the neuron model of Eugene M. Izhikevich as the spiking neural network model, the EEG signals were processed biologically realistically.