PSR-based research of feature extraction from one-second EEG signals: a neural network study

Abstract

The speed and accuracy of signal classification are the most valuable parameters to create real-time systems for interaction between the brain and the computer system. In this work, we propose a schema of the extraction of features from one-second electroencephalographic (EEG) signals generated by facial muscle stress. We have tested here three sorts of EEG signals. The signals originate from different facial expressions. The phase-space reconstruction (PSR) method has been used to convert EEG signals from these three classes of facial muscle tension. For further processing, the data has been converted into a two-dimensional (2D) matrix and saved in the form of color images. The 2D convolutional neural network (CNN) served to determine the accuracy of the classifications of the previously unknown PSR generated images from the EEG signals. We have witnessed an improvement in the accuracy of the signal classification in the phase-space representation. We have found that the CNN network better classifies colored trajectories in the 2D phase-space graph. At the end of this work, we compared our results with the results obtained by a one-dimensional convolution neural network.