Authors:
Semen A. Kurkin
;
Elena N. Pitsik
;
Vyacheslav Yu. Musatov
;
Anastasia E. Runnova
and
Alexander E. Hramov
Affiliation:
Research and Education Center “Artificial Intelligence Systems and Neurotechnology”, Yuri Gagarin State Technical University of Saratov, 77 Politechnicheskaya str., Saratov and Russia
Keyword(s):
Artificial Neural Networks, Brain-Computer Interface, EEG Analysis, Brain Activity, Multilayer Perceptron, Radial Basis Function, Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Human-Machine Interfaces
;
Human-Robots Interfaces
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Neural Networks Based Control Systems
;
Robotics and Automation
Abstract:
Recognition of human brain activity associated with imaginary or real movements is a complex task that requires an accurate and conscious choice of analysis approach. Recent researches revealed the great potential of machine learning algorithms for electroencephalography data analysis due to the ability of these methods to establish nonlinear and nonstationary correlations, and the most attention is focused on artificial neural networks (ANNs). Here, we introduce the ANN-based method 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 and support vector mac
hine. 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. Obtained results provide better insight on neural networks potential for integration in brain-computer interfaces that are based on EEG patterns recognition.
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