Erbslöh Andreas, Zimmermann Julius, Ingebrandt Sven, Mokwa Wilfried, Seidl Karsten, van Rienen Ursula, Schiele Gregor, Kokozinski Rainer
Intelligent Embedded Systems Lab, University of Duisburg-Essen, Duisburg, Germany.
Institute of General Electrical Engineering, University of Rostock, Rostock, Germany.
J Neural Eng. 2025 Apr 11;22(2). doi: 10.1088/1741-2552/adc2d5.
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.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.We obtained consistent results for relevant electrochemical using electrochemical impedance spectroscopy, time-domain analysis and Fourier-transform-based impedance estimation. Moreover, the numerical simulations confirmed that the measured microelectrode had the expected properties.Our results pave the way to enable a live assessment of the impedance in future MEA-based neural devices. This will enable adaptive electrical stimulation or (re-)selection of recording electrodes by taking the actual state of the electrode into account.
现代神经装置能够与退化组织相互作用，以恢复感觉丧失功能，并利用微电子阵列（MEA）抑制神经退行性疾病的症状。它们具有双向接口，用于进行电刺激以写入新信息，以及记录神经活动以读出神经任务，例如运动意图。对于这两种应用，电极 - 组织界面（ETI）的电阻抗至关重要。然而，由于封装效应和神经元结构的变化，ETI在运行期间可能会发生变化。我们研究了在使用微电极阵列进行刺激时，是否可以从记录中可靠地提取阻抗谱。我们提出了一种用于表征刺激期间电阻抗谱的测量方法。我们在电解质溶液中使用穿透式微电极阵列进行电荷控制刺激。从刺激记录中，我们提取了阻抗。此外，建立了刺激电极的数值模型（数字孪生）。我们使用电化学阻抗谱、时域分析和基于傅里叶变换的阻抗估计，对相关电化学过程获得了一致的结果。此外，数值模拟证实了所测量的微电极具有预期的特性。我们的结果为在未来基于MEA的神经装置中实现对阻抗的实时评估铺平了道路。这将通过考虑电极的实际状态来实现自适应电刺激或（重新）选择记录电极。
