Artificial intelligence systems for classifying EEG responses to imaginary and real movements of operators

Abstract

Here, we introduce the method based on artificial neural networks (ANNs) for recognition and classification of patterns in electroencephalograms (EEGs) associated with imaginary and real movements of untrained volunteers. In order to get the fastest and the most accurate classification performance of multichannel motor imagery EEG-patterns, we propose our approach to selection of appropriate type, topology, learning algorithm and other parameters of neural network. We considered linear neural network, multilayer perceptron, radial basis function network (RBFN) and support vector machine. We revealed that appropriate quality of recognition can be obtained by using particular groups of electrodes according to extended international 10−10 system. Besides, pre-processing of EEGs by low-pass filter can significantly increase the classification performance. We developed mathematical model based on ANN for classification of EEG patterns corresponding to imaginary or real movements, which demonstrated high efficiency for untrained subjects. Achieved recognition accuracy of movements was up to 90−95% for group of subjects. RBFN demonstrated more accurate classification performance in both cases. Pre-filtering of input data using low-pass filter significantly increases recognition accuracy on 10−20% in average, and the low-pass filter with cutoff frequency 4 Hz shows the best results. It was revealed that using different sets of electrodes placed on different brain areas and consisted of 6-12 channels, one can achieve close to maximal classification accuracy. It is convenient to use electrodes on frontal and temporal lobes for real movements, and several sets containing 6-9 electrodes — in case with imagery movements.