Fractional-Order Model Predictive Control for Neurophysiological Cyber-Physical Systems: A Case Study using Transcranial Magnetic Stimulation

TL;DR

This paper introduces a fractional-order model predictive control approach for neurophysiological cyber-physical systems, specifically targeting seizure mitigation through EEG-based closed-loop transcranial magnetic stimulation, demonstrating improved robustness and effectiveness.

Contribution

It presents a novel fractional-order MPC framework for TMS, integrating EEG data for closed-loop brain activity regulation, and compares its performance with open-loop strategies in seizure mitigation.

Findings

Fractional-order MPC effectively mitigates seizure-like events in simulations.

The proposed method shows increased robustness over traditional strategies.

Simulation results demonstrate improved control of brain activity.

Abstract

Fractional-order dynamical systems are used to describe processes that exhibit temporal long-term memory and power-law dependence of trajectories. There has been evidence that complex neurophysiological signals like electroencephalogram (EEG) can be modeled by fractional-order systems. In this work, we propose a model-based approach for closed-loop Transcranial Magnetic Stimulation (TMS) to regulate brain activity through EEG data. More precisely, we propose a model predictive control (MPC) approach with an underlying fractional-order system (FOS) predictive model. Furthermore, MPC offers, by design, an additional layer of robustness to compensate for system-model mismatch, which the more traditional strategies lack. To establish the potential of our framework, we focus on epileptic seizure mitigation by computational simulation of our proposed strategy upon seizure-like events. We conclude by empirically analyzing the effectiveness of our method, and compare it with event-triggered open-loop strategies.