Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement
Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly conside...
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doaj-3757fe3a6de84f86bd2f5df725b42a272020-11-25T01:46:31ZengFrontiers Media S.A.Frontiers in Systems Neuroscience1662-51372019-11-011310.3389/fnsys.2019.00066482838Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During MovementMaitreyee WairagkarYoshikatsu HayashiSlawomir J. NasutoElectroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is 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 modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling 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. A 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 the earliest movement 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.https://www.frontiersin.org/article/10.3389/fnsys.2019.00066/fullLong-Range Temporal Correlation (LRTC)Short-Range Dependence (SRD)Autoregressive Fractionally Integrated Moving Average (ARFIMA)electroencephalography (EEG)Brain Computer Interface (BCI)movement intention |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Maitreyee Wairagkar Yoshikatsu Hayashi Slawomir J. Nasuto |
spellingShingle |
Maitreyee Wairagkar Yoshikatsu Hayashi Slawomir J. Nasuto Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement Frontiers in Systems Neuroscience Long-Range Temporal Correlation (LRTC) Short-Range Dependence (SRD) Autoregressive Fractionally Integrated Moving Average (ARFIMA) electroencephalography (EEG) Brain Computer Interface (BCI) movement intention |
author_facet |
Maitreyee Wairagkar Yoshikatsu Hayashi Slawomir J. Nasuto |
author_sort |
Maitreyee Wairagkar |
title |
Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement |
title_short |
Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement |
title_full |
Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement |
title_fullStr |
Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement |
title_full_unstemmed |
Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement |
title_sort |
modeling the ongoing dynamics of short and long-range temporal correlations in broadband eeg during movement |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Systems Neuroscience |
issn |
1662-5137 |
publishDate |
2019-11-01 |
description |
Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is 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 modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling 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. A 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 the earliest movement 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. |
topic |
Long-Range Temporal Correlation (LRTC) Short-Range Dependence (SRD) Autoregressive Fractionally Integrated Moving Average (ARFIMA) electroencephalography (EEG) Brain Computer Interface (BCI) movement intention |
url |
https://www.frontiersin.org/article/10.3389/fnsys.2019.00066/full |
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