Work

Non-Linear Modeling and Precision Analysis Approach for Implantable Multi-Channel Neural Recording Systems

Year: 2025

Type: article

Abstract: High-precision implantable multi-channel neural recording systems are considered as having a crucial role in the diagnosis and treatment of neurological disorders. However, it is a significant design challenge to achieve an optimal trade-off among linear parameters, signal fidelity, power consumption, and circuit area. To address this challenge, a Simulink-based modeling approach has been proposed to incorporate adjustable non-linear parameters across the front-end circuits and analog-to-digital converter (ADC) stages. The model evaluates non-linearity impacts on system performance through both quantitative spike detection accuracy analysis and a neural decoding paradigm based on Chinese handwriting reconstruction. Simulated results show that total harmonic distortion (THD) can be set to -34.32 dB for the low-noise amplifier (LNA), -33.73 dB for the programmable gain amplifier (PGA), and -57.95 dB for the ADC in order to achieve reliable detection accuracy with minimal design cost. Moreover, ADC non-linearity has a greater influence on system performance than that of the LNA and PGA. The proposed approach offers quantitative and systematic hardware design guidance to balance signal fidelity and resource efficiency for future low-power, high-accuracy neural recording systems.

Source: Micromachines

DOI: 10.3390/mi16101176

Authors: Jinyan He, Jian Xu, Yueming Wang

Institutions: Zhejiang University, Hangzhou Seventh Peoples Hospital

Language: English

Topic: Analog and Mixed-Signal Circuit Design

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,534

Funders: National Natural Science Foundation of China, Zhejiang University, Fundamental Research Funds for the Central Universities