Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

Abstract

Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature. We investigate and validate changes in LRTCs during five different types of movements and motor imagery tasks using two independent EEG datasets recorded with two different paradigms—our finger tapping dataset with single self-initiated asynchronous finger taps and publicly available EEG dataset containing cued continuous movement and motor imagery of fists and feet. We quantified instantaneous changes in broadband LRTCs by detrended fluctuation analysis on single trial 2 s EEG sliding windows. The broadband LRTC increased significantly (p < 0.05) during all motor tasks as compared to the resting state. In contrast, the alpha oscillation LRTC, which had to be computed on longer stitched EEG segments, decreased significantly (p < 0.05) consistently with the literature. This suggests the complementarity of underlying fast and slow neuronal scale-free dynamics during movement and motor imagery. The single trial broadband LRTC gave high average binary classification accuracy in the range of 70.54±10.03% to 76.07±6.40% for all motor execution and imagery tasks and hence can be used in brain–computer interface (BCI). Thus, we demonstrate generalizability, robustness, and reproducibility of novel motor neural correlate, the single trial broadband LRTC, during different motor execution and imagery tasks in single asynchronous and cued continuous motor-BCI paradigms and its contrasting behavior with LRTC in alpha oscillation amplitude.

Functional electrical stimulation driven by a brain–computer interface in acute and subacute stroke patients impacts beta power and long-range temporal correlation

“…LRTC can be quantified in EEG data in the frequency domain by estimating the slope of the 1/f power spectrum on a log-log scale and computing the scaling exponent. A DFA log-log plot is calculated at different time scales from the residual fluctuations of the locally linearly detrended signal and yields a quantification of the Hurst exponent [6], [16]. Calculation of the Hurst exponent 𝐻 provides a more practical and therefore the most common approach to estimating LRTC in non-stationary signals and has been shown to yield consistently related results [17].…”

“…The amplitude of alpha and beta oscillations shows not only intermittent fluctuations (ERD/ERS) associated with movement, but also long-range temporal correlation (LRTC), which differs during different motor tasks and compared with rest [6], [7]. LRTC can be considered as a type of electrophysiological memory [7].…”

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification

“…Recently, Transformer-based models have been developed for object detection [29], image classification [30], and protein engineering [31], suggesting their wide applicability. Time series such as EEG signals have long-range dependencies, which can be characterized by estimating the Long-Range Temporal Correlation (LRTC) [32,33]. LRTC has been indeed observed in the EEG and becomes stronger during voluntary movement and motor imagery [32,33].…”

“…Time series such as EEG signals have long-range dependencies, which can be characterized by estimating the Long-Range Temporal Correlation (LRTC) [32,33]. LRTC has been indeed observed in the EEG and becomes stronger during voluntary movement and motor imagery [32,33]. The spatial linear and nonlinear dependencies have been observed in time-series EEG signals [34,35], showing inter-channel correlation [36,37].…”

Periodic and non-periodic brainwaves emerging via random syncronization of closed loops of firing neurons

“…Recent studies have suggested that simultaneous changes of aperiodic and periodic brainwave components can underpin changes in functional and behavioural features, with the broadband components modulated by task performance and correlated with neuronal spiking activity. Synchronization between different neuronal groups may also manifest within arrhythmic brain activity with no apparent periodicity [6][7][8][9][10][11][12][13]. To keep pace with these findings, algorithms are being developed to the purpose of breaking complex electrophysiological signals down and transferring scientific findings into clinical practices [14,15], an issue of is increasingly relevant to the development of brain-machine interfaces [16].…”