Prediction of impedance characteristic during electrical stimulation with microelectrode arrays.

Abstract

Objective: Modern neural devices allow to interact with degenerated tissue in order to restore sensoric loss function and to suppress symptoms of neurodegenerative diseases using microelectronic arrays (MEA). They have a bidirectional interface for performing electrical stimulation to write-in new information and for recording the neural activity to read-out a neural task, e.g. movement ambitions. For both applications, the electrical impedance of the electrode-tissue interface (ETI) is crucial. However, the ETI can change during run-time due to encapsulation effects and changes of the neuronal structures. We investigated if an impedance spectrum can be reliably extracted from recordings during stimulation with microelectrode arrays.

Approach: We present a measurement method for characterizing the electrical impedance spectrum during stimulation. We performed charge-controlled stimulation with a penetrating microelectrode array in an electrolyte solution. From the stimulation recordings, we extracted the impedance. Furthermore, a numerical model (digital twin) of the stimulation electrodes is established. Main results. We obtained consistent results for relevant electrochemical using electrochemical impedance spectroscopy, time-domain analysis and Fourier-transformbased impedance estimation. Moreover, the numerical simulations confirmed that the measured microelectrode had the expected properties. Significance. Our results pave the way to enable new functionalities in future MEA-based neural devices For example, adaptive electrical stimulation or (re-)selection of recording electrodes can be supported by taking the actual state of the electrode into account. .