Method for automatic detection of movement-related EEG pattern time boundaries

• Published in: Soft computing (Berlin, Germany) Vol. 28; no. 5; pp. 4489 - 4501
• Main Authors: Shcherban, I. V., Lazurenko, D. M., Shcherban, O. G., Shaposhnikov, D. G., Kirilenko, N. E., Shustova, A. V.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024; Springer Nature B.V
• Subjects: Adaptive search techniques; Application of Soft Computing; Artificial Intelligence; Automation; Bandpass filters; Boundaries; Computational Intelligence; Control; Electroencephalography; Engineering; Fourier transforms; Human-computer interface; Machine learning; Mathematical Logic and Foundations; Mechatronics; Motion perception; Robotics; Search methods; Spectrum analysis; Synthesis; Wavelet transforms; Singular spectrum analysis; Initial and final time boundaries; Electroencephalogram; Hausdorff distance; Adaptive band-pass filter; Human voluntary motor activity; Movement-related brain potentials; Automatic detection
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-08837-y

The study was aimed at developing a new automatic search technique for specific invariant patterns of movement-related brain potentials reflected in multidimensional electroencephalogram (EEG) signals. An adaptive band-pass filter with bandwidth closely matching the spectrum of the desired EEG pattern at the observed moment was synthesized based on the Singular Spectrum Analysis methodology. The preliminary filtering of the original EEG signals provides the required sensitivity for subsequent searching of time boundaries in patterns. The correctness of the developed method was confirmed with standard machine learning tools through the validation of the adaptive search method carried out on the general set of initial data. It is shown that the synthesized method has provided a reliable automatic search for induced pre-movement EEG patterns and the correct determination of their time boundaries (accuracy up 29% on average and reached maximum values to 100% for some individuals). The developed method expands the existing tools to improve the functionality and reliability of various Brain-computer interfaces for various purposes, including medical applications for paralyzed patients.