Features of motor-related brain activity revealed via recurrence quantification analysis

We propose an approach for motor-related brain activity analysis based on the combination of continuous wavelet transform and recurrence quantification analysis (RQA). Detecting such patterns on EEG is a complex task due to the nonstationarity and complexity of EEG signal, which leads to high inter- and intra-subject variability of traditionally applied methods. We show that RQA measures of complexity, such as recurrence rate an laminarity, are very useful in detection of transitions from background to motor-related EEG. Moreover, RQA measures time dependence for upper limbs is contralateral, which allows us to distinguish two types of movements.