Quantification of Fractional Dynamical Stability of EEG Signals as a Bio-Marker for Cognitive Motor Control
Emily Reed (University of Southern California)
Paul Bogdan (University of Southern California)
Sergio Pequito (TU Delft - Team Sergio Pequito)
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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.
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