Recognition of electroencephalographic patterns related to human movements or mental intentions with multiresolution analysis

A.N. Pavlov, D.S. Grishina, A.E. Runnova, V.A. Maksimenko, O.N. Pavlova, N.V. Shchukovsky, A.E. Hramov and J. Kurths

Chaos, Solitons & Fractals, 2019, vol. 126, issue C, 230-235

Abstract: We study the problem of recognizing specific oscillatory patterns in multichannel electroencephalograms (EEGs) of untrained volunteers arising during various types of movements and mental intentions that are associated with motor functions. To distinguish between the related patterns, we perform a multiresolution analysis based on discrete wavelet transform with the Daubechies basic functions. Using the standard deviation of the wavelet coefficients characterizing their variability in non-overlapping ranges of scales, we verify the ability to separate EEG segments during real and imaginary movements from the background EEG, which appeared in most recording channels. Recognizing the type of movement, such as, e.g., imaginary movement (i.e., the movement that a person performs mentally) by right arm or left leg, is a more complicated task that often can only be solved in few channels. Nevertheless, such recognition was demonstrated for real movements using about 6–8 channels out of 32, and for mental intentions using 1–2 channels. To improve the recognition of various imaginary movements, preliminary training seems mandatory.

Keywords: Oscillatory patterns; Recognition; Motor functions; Wavelet; EEG; Multiresolution analysis, (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:126:y:2019:i:c:p:230-235

DOI: 10.1016/j.chaos.2019.06.016

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