Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Vladimir A. Maksimenko,Tatyana Yu. Efremova,Semen A. Kurkin,Alexander N. Pisarchik,Alexander E. Hramov,Vyacheslav Yu. Musatov,Elena N. Pitsik,Anastasia E. Runnova

doi:10.1155/2018/9385947

Abstract

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high-frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

Highlights

The development of brain-computer interfaces (BCIs) is a very challenging and important task of neuroscience and neurotechnology
The EEG trials were classified into two groups with the help of artificial neural network (ANN) with different configurations: support vector machine (SVM), MP, radial basis function (RBF), and linear network (LN)
The results obtained for SVM, RBF, and Multilayer perceptron (MLP) demonstrated averaged classification accuracy of 76.5%, 77.9%, and 72.4%, respectively

Summary

Introduction

The development of brain-computer interfaces (BCIs) is a very challenging and important task of neuroscience and neurotechnology. BCI is based on the analysis of noninvasive electroencephalography (EEG) signals recorded by electrodes placed on skin surface of a head. The treatment of multichannel EEG signals is a very sophisticated task because they are nonstationary, high-dimensional, and extremely noisy [7, 8]. All these factors make difficult the recognition and classification of specific motor-related or percept-related patterns in a single trial mode [9, 10] and require extensive statistical measures

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Discussion

Conclusion