AUTHOR=Wairagkar Maitreyee , Hayashi Yoshikatsu , Nasuto Slawomir J. 

TITLE=Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement

JOURNAL=Frontiers in Systems Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2019.00066

DOI=10.3389/fnsys.2019.00066

ISSN=1662-5137

ABSTRACT=Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterisation of such changes has been focusing mostly on narrowband spectral processes such as event-related desynchronisation (ERD) in the sensorimotor rhythms because EEG was mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection. The long-range temporal correlations (LRTC) are ubiquitously present in several neuronal processes, typically requiring longer timescales to detect. In this paper, we study the ongoing changes in the dynamics of long- as well as short-range temporal dependencies in the single-trial broadband EEG during movement intention. We obtained LRTC in 2 s windows of broadband EEG and modelled it using autoregressive fractionally integrated moving average (ARFIMA) model which allowed simultaneous modelling of short- and long-range temporal correlations. There were significant (p<0.05) changes in both broadband long- and short-range temporal correlations during movement intention and execution. We discovered that the broadband LRTC and narrowband ERD are complementary processes providing distinct information about movement because eliminating LRTC from the signal did not affect the ERD and conversely, eliminating ERD from the signal did not affect LRTC. Exploring the possibility of applications in brain computer interfaces (BCI), we used hybrid features with combinations of LRTC, ARFIMA, and ERD to detect movement intention. Significantly higher (p<0.05) classification accuracy of 88.3+-4.2% was obtained using the combination of ARFIMA and ERD features together, which also predicted movement earliest at 1 s before its onset. The ongoing changes in the long- and short-range temporal correlations in broadband EEG contribute to effectively capturing the motor command generation and can be used to detect movement successfully. These temporal dependencies provide different and additional information about the movement.