Classification of hand motions in EEG signals using recurrent neural networks

Abstract

This paper describes hand motion detection and the method for classification of 32-component EEG signals. This method is based on using recurrent convolution neural network as multi-class classifier. In this paper, we propose and empirically evaluate several architectures of recurrent convolutional neural network, and show advantages of using recurrent convolutional neural network for investigating problem. The results prove that this type of classifier can effectively distinguish characteristic features in the initial EEG signals and provide correct values of neural network outputs. Using recurrent convolution layer instead of the standard convolution layer can significantly improve the quality of classification. Adding recurrent connections for convolutional layer neurons increases the depth of the network, maintaining a constant number of parameters by weight sharing between layers.