ArticleFrontiers in systems neuroscience2019
Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement.
Article in Frontiers in systems neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Subtle to significant: Enhancing heaviness perception with stochastic resonance.Journal of experimental psychology. Human perception and performance · 2026Article
- Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.Frontiers in neuroscience · 2024Article
- Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks.Frontiers in neuroscience · 2021Article
- Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation.Computational intelligence and neuroscience · 2021Article
- Collective almost synchronization-based model to extract and predict features of EEG signals.Scientific reports · 2020Article
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Authors and funding
3 authors.
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Abstract
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 (
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