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

Journal article (2022)

Authors

Emily Reed University of Southern California
Paul Bogdan University of Southern California
S.D. Gonçalves Melo Pequito Team Sergio Pequito - Mechanical, Maritime and Materials Engineering
Research Group
Team Sergio Pequito (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Emily A. Reed, Paul Bogdan, S.D. Gonçalves Melo Pequito
DOI
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https://doi.org/10.3389/fcteg.2021.787747
Stability Nonlinear models EEG signals Control applications Cognitive motor control
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Published Date

2022

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Sergio Pequito

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.