Eur. Phys. J. Plus (2021) 136: 408
https://doi.org/10.1140/epjp/s13360-021-01423-x

Regular Article

Effects of healthy aging on electrical activity of the brain during motor tasks characterized with wavelets

¹ Saratov State University, Astrakhanskaya Str. 83, 410012, Saratov, Russia
² Regional Scientific and Educational Mathematical Center “Mathematics of Future Technologies”, 23 Gagarina Avenue, 603950, Nizhny Novgorod, Russia
³ Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Universitetskaya Str. 1, 420500, Innopolis, The Republic of Tatarstan, Russia
⁴ Northwestern Polytechnical University, 27 West Youyi Road, Beilin District, 710072, Xi’an, Shaanxi, China
⁵ Saratov State Medical University, Bolshaya Kazachya Str. 112, 410012, Saratov, Russia

^a pavlov.alexeyn@gmail.com

Received: 6 November 2020
Accepted: 10 April 2021
Published online: 19 April 2021

Abstract

Age-related changes in the brain’s electrical activity can be caused by healthy aging and brain disorders. The ability to detect such phenomena from an electroencephalogram (EEG) is important to identify diseases’ latent stages. These changes appear in the brain’s background electrical activity, but the performance of cognitive or motor tasks can cause more significant signs of impairments in brain dynamics. Here, we analyze the features of multichannel EEGs in groups of healthy elderly and younger adults during hand clenching and apply two wavelet-based methods to reveal distinctions that arise with age, namely multiresolution analysis using the discrete wavelet transform, and multifractal formalism, which involves extracting the skeleton of the continuous wavelet transform. With the first method, we demonstrate that inter-group differences are established at rest and during the performance of motor tasks. With the second method, we also find similar distinctions, although the number of suitable channels and their distribution can differ. We conclude that characterization of age-related differences depends on the wavelet-based method used for EEG processing.