HAND MOTIONS DETECTION IN EEG SIGNALS USING CONVOLUTIONAL NEURAL NETWORKS
This paper describes hand motions detection method in 32-component EEG signals. This method is based on using ensemble of convolution neural networks as multi-class classifier. In this paper we proposed and empirically evaluated several architectures of convolutional neural network as well as ensemble of these models based on blending algorithm and logistic regression as final classifier, shown advantages of using convolutional neural network for investigating problem. The results suggest that ensemble of several convolutional neural networks can effectively distinguish characteristic features in the initial EEG signals and provide right values of classifier outputs. Using blending algorithm allows to get the best results by integrating the classification of the best predictions of several models that are individually on the entire test set may not give the best result.