Personalized closed-loop brain stimulation system based on linear state space model identification

摘要

The closed-loop brain stimulation system can adjust stimulation parameters based on neural activity feedback, so as to provide precise and personalized treatment for neurological and neuropsychiatric diseases. The design of a model-based closed-loop system requires the choice of a suitable system identification framework to learn a dynamic mapping from input and output data to quantify the effect of input stimulation on output neural activity, and design the controller with the identification model. We first designed a control-theory system identification framework to build a dynamic input-output (IO) model of neural activity suitable for closed-loop control design. To achieve effective model-based control, we use a linear state space model (LSSM), which uses low-dimensional hidden neural states to characterize the effect of inputs on neural activity. Secondly, in order to train the model parameters, we choose a pulse sequence with independent modulation of parameters by pseudo-random sequence (PRS), and proved that this is the best choice for collecting informational IO data sets in system identification. The input waveform can be used as a deep brain stimulation (DBS) for treating neuropsychiatric diseases while meeting clinical safety requirements. Thirdly, we take the beta frequency band of the local field potential (LFP) as a biomarker of Parkinson's disease (PD) and system feedback signal to identify the system and design a closed-loop controller. The simulation results show that the closed-loop controller designed based on the LSSM model identified by the PRS modulation waveform can achieve the ideal control effect, and the performance of the model is similar to that of a real physiological IO model describing neural activity. The new PRS modulation waveform and system identification framework can help develop future model-based closed-loop stimulation systems, which is of great significance for the treatment of neuropsychiatric diseases.