دورية أكاديمية
Modeling the ongoing dynamics of short and long-range temporal correlations in broadband EEG during movement
| العنوان: | Modeling the ongoing dynamics of short and long-range temporal correlations in broadband EEG during movement |
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| المؤلفون: | Wairagkar, M, Hayashi, Y, Nasuto, SJ |
| بيانات النشر: | Frontiers Media |
| سنة النشر: | 2019 |
| المجموعة: | Imperial College London: Spiral |
| مصطلحات موضوعية: | Science & Technology, Life Sciences & Biomedicine, Neurosciences, Neurosciences & Neurology, Long-Range Temporal Correlation (LRTC), Short-Range Dependence (SRD), Autoregressive Fractionally Integrated Moving Average (ARFIMA), electroencephalography (EEG), Brain Computer Interface (BCI), movement intention, broadband, single trial, COMPUTER INTERFACE BCI, TIME-SERIES, FEATURE-EXTRACTION, SCALING BEHAVIOR, BRAIN, DEPENDENCE, DESYNCHRONIZATION, OSCILLATIONS, EXPONENTS |
| الوصف: | 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1662-5137 |
| العلاقة: | Frontiers in Systems Neuroscience; http://hdl.handle.net/10044/1/86635Test |
| DOI: | 10.3389/fnsys.2019.00066 |
| الإتاحة: | https://doi.org/10.3389/fnsys.2019.00066Test http://hdl.handle.net/10044/1/86635Test |
| حقوق: | © 2019 Wairagkar, Hayashi and Nasuto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. ; https://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: | edsbas.53418995 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 16625137 |
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| DOI: | 10.3389/fnsys.2019.00066 |