Dealing with State Estimation in Fractional-Order Systems under Artifacts
For researchers working with fractional-order models of neurophysiological signals, this work offers a theoretical and algorithmic solution to the practical problem of artifact contamination.
The paper provides necessary and sufficient conditions for state estimation in discrete-time fractional-order dynamical systems with artifacts, and proposes an algorithm that successfully estimates states despite artifacts, demonstrated on real EEG data.
Fractional-order dynamical systems are used to describe processes that exhibit long-term memory with power-law dependence. Notable examples include complex neurophysiological signals such as electroencephalogram (EEG) and blood-oxygen-level dependent (BOLD) signals. When analyzing different neurophysiological signals and other signals with different origin (for example, biological systems), we often find the presence of artifacts, that is, recorded activity that is due to external causes and does not have its origins in the system of interest. In this paper, we consider the problem of estimating the states of a discrete-time fractional-order dynamical system when there are artifacts present in some of the sensor measurements. Specifically, we provide necessary and sufficient conditions that ensure we can retrieve the system states even in the presence of artifacts. We provide a state estimation algorithm that can estimate the states of the system in the presence of artifacts. Finally, we present illustrative examples of our main results using real EEG data.