Motor-related elderly brain activity revealed via recurrence quantification analysis

Abstract

We study motor-related brain activity in the group of elderly individuals (aged 55-76) using the continuous wavelet transform and the recurrence quantification analysis (RQA). Detecting motor patterns on electroencephalograms (EEGs) is a complex task due to the nonstationarity and complexity of EEG signal, which leads to the high inter- and intra-subject variability of traditionally applied methods. It is especially demanded to use these methods in the context of the elderly group analysis due to the additional age-related changes of the brain motor cortex functioning. In the present paper, we show that RQA measure of complexity is very useful in detection of transitions from background (normal) to motor-related brain activity captured via EEG signals. Moreover, used RQA measure of determinism calculated to quantify brain processes during upper limbs movements reflects contralateral properties of motor-related neuronal activity, which is helpful at distinguishing between two types of executed movements.