Convolutional Neural Networks for Four-Class Motor Imagery Data Classification

Abstract

In this paper the use of convolutional neural networks (CNN) is discussed in order to solve four class motor imagery classification problem. Analysis of viable CNN architectures and their influence on the obtained accuracy for the given task is argued. Furthermore, selection of optimal feature map image dimension, filter sizes and other CNN parameters used for network training is investigated. Methods for generating 2D feature maps from 1D feature vectors are presented for commonly used feature types. Initial results show that CNN can achieve high 68% classification accuracy for the four class motor imagery problem with less complex feature extraction techniques. It is shown that optimal accuracy highly depends on feature map dimensions, filter sizes, epoch count and other tunable factors, therefore various fine-tuning techniques must be employed. Experiments show that simple FFT energy map generation techniques are enough to reach the state-of-the-art classification accuracy for common CNN feature map sizes. This work also confirms that CNNs are able to learn a descriptive set of information needed for optimal electroencephalogram (EEG) signal classification.