Quantification of Fractional Dynamical Stability of EEG Signals as a Bio-Marker for Cognitive Motor Control

Emily A. Reed, Paul Bogdan, S.D. Gonçalves Melo Pequito*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Assessing the stability of biological system models has aided in uncovering a plethora of new insights in genetics, neuroscience, and medicine. In this paper, we focus on analyzing the stability of neurological signals, including electroencephalogram (EEG) signals. Interestingly, spatiotemporal discrete-time linear fractional-order systems (DTLFOS) have been shown to accurately and efficiently represent a variety of neurological and physiological signals. Here, we leverage the conditions for stability of DTLFOS to assess a real-world EEG data set. By analyzing the stability of EEG signals during movement and rest tasks, we provide evidence of the usefulness of the quantification of stability as a bio-marker for cognitive motor control.
Original languageEnglish
Article number787747
Number of pages11
JournalFrontiers in Control Engineering
Volume2
DOIs
Publication statusPublished - 2022

Keywords

  • nonlinear models
  • stability
  • EEG signals
  • control applications
  • cognitive motor control

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