Convolutional Neural Networks with Reusable Full-Dimension-Long Layers for Feature Selection and Classification of Motor Imagery in EEG Signals

Abstract

In the present article the author addresses the task of classification of motor imagery in EEG signals by proposing innovative architecture of neural network. Despite all the successes of deep learning, neural networks of significant depth could not ensure better performance compared to shallow architectures. The approach presented in the article employs this idea, making use of yet shallower, but productive architecture. The main idea of the proposed architecture is based on three points: full-dimension-long ‘valid’ convolutions, dense connections - combination of layer’s input and output and layer reuse. Another aspect addressed in the paper is related to interpretable machine learning. Interpretability is extremely important in medicine, where decisions must be taken on the basis of solid arguments and clear reasons. Being shallow, the architecture could be used for feature selection by interpreting the layers’ weights, which allows understanding of the knowledge about the data cumulated in the network’s layers. The approach, based on a fuzzy measure, allows using Choquet integral to aggregate the knowledge generated in the layer weights and understanding which features (EEG electrodes) provide the most essential information. The approach allows lowering feature number from 64 to 14 with an insignificant drop of accuracy (less than a percentage point).