Motor execution reduces EEG signals complexity: Recurrence quantification analysis study

Abstract

The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain-computer interface (BCI) applications. Even though some well-known features of motor-related electroencephalograms (EEGs) have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of µ-rhythm in the sensorimotor cortex, i.e. tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of µ-band neuronal activity. This is due to a lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this theses we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and therefore are able to classify the laterality of executed movements.

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

“…While averaged trials are usually exhibit a clearly pronounced difference between various types of movements (e.g., with left/right hand motor imagery), in the case of single trials, the classification problem is more drastic due to a high variability of EEG or MEG brain signals during imagination, as well as the existence of strong noise. Typically, the classification accuracy does not exceed 80% when special mathematical methods are applied, such as, e.g., SVM machines [55], wavelets [36,56], multilayer perceptrons [4], and recurrence quantitative measures [38].…”

“…It is well known that there is a strong correlation between EEG rhythmic activity and functional states of the organism [31][32][33] that can be used for revealing specific features related to real and imagery motor activity [34][35][36]. The problem of detection and classification of different types of motor execution requires the application of various methods for time-frequency and spatio-temporal analyses [37], including artificial intelligence methods [4], recurrence measures of signal complexity [38], as well as event-related synchronization (ERS) and event-related desynchronization (ERD) [39].In this work, we use fNIRS, an efficient noninvasive technique for brain activity estimation [40], that employs near-infrared light to detect changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin levels due to hemodynamic brain activity and the rapid delivery of oxygenated blood to active cortical areas through neurovascular coupling [41]. A high efficiency of fNIRS is achieved due to the use of laser lights with two different wavelengths which penetrate most tissues in the head, but are highly absorbed by oxyhemoglobin (HbO) and deoxyhemoglobin (HbR).…”

“…It is well known that there is a strong correlation between EEG rhythmic activity and functional states of the organism [31][32][33] that can be used for revealing specific features related to real and imagery motor activity [34][35][36]. The problem of detection and classification of different types of motor execution requires the application of various methods for time-frequency and spatio-temporal analyses [37], including artificial intelligence methods [4], recurrence measures of signal complexity [38], as well as event-related synchronization (ERS) and event-related desynchronization (ERD) [39].…”

Age-related changes in the motor planning strategy slow down motor initiation in elderly adults

“…Exploring these mechanisms 25 is crucial to deeper understand motor control in humans. Motor planning is also 26 subjected to the age-related changes due to the following: (i) motor initiation process 27 involves many higher cognitive functions such as sensory processing, working memory, 28 motor embodiment, and sensorimotor integration [18][19][20][21], which are known to decline 29 strongly with age; (ii) the theta activity underlying the majority of these processes 30 exhibits significant age-related changes -abnormally increased theta activity in elderly 31 people indicates subjective cognitive dysfunction and suspected dementia [22,23]. 32 Based on the above, we hypothesize that the age-related changes in the motor 33 planning mechanism also affect the slowing of the motor initiation phase in elderly 34 adults.…”

“…A priory knowledge about the 124 cortical activation during movements execution implies that motor brain response is 125 determined as a pronounced event-related desynchronization (ERD) of mu-oscillations 126 in the contralateral area of the motor cortex. Notably, a wide body of EEG studies 127 reports that symmetrical C4 and C3 sensors evidence brain motor response in case of 128 the left-and right-hand movements, respectively [28][29][30][31]. Here, we used mu-band 129 event-related spectral power (ERSP µ ) at C4 and C3 sensors to estimate motor brain 130 response time (MBRT) associated with LH and RH conditions for each subject of both 131 groups.…”