Non-Linear Modeling and Precision Analysis Approach for Implantable Multi-Channel Neural Recording Systems
Jinyan He1,2, Jian Xu2,3,4, Yueming Wang4,5
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Micromachines
|October 29, 2025
Summary
This study presents a Simulink model for designing neural recording systems. It optimizes trade-offs between signal fidelity and power consumption by adjusting non-linear parameters, crucial for neurological disorder diagnosis.
Area of Science:
- Biomedical Engineering
- Neuroscience Instrumentation
- Signal Processing
Background:
- High-precision implantable neural recording systems are vital for neurological disorder diagnosis and treatment.
- Optimizing linear parameters, signal fidelity, power consumption, and circuit area presents a significant design challenge.
Purpose of the Study:
- To propose a Simulink-based modeling approach for optimizing neural recording system design.
- To evaluate the impact of adjustable non-linear parameters on system performance.
Main Methods:
- Developed a Simulink model incorporating adjustable non-linear parameters in front-end circuits and analog-to-digital converter (ADC) stages.
- Assessed non-linearity effects using quantitative spike detection accuracy and a neural decoding paradigm (Chinese handwriting reconstruction).
Main Results:
- Determined optimal total harmonic distortion (THD) levels for reliable detection: -34.32 dB (LNA), -33.73 dB (PGA), and -57.95 dB (ADC).
- Found that ADC non-linearity significantly impacts system performance more than LNA and PGA non-linearity.
Conclusions:
- The proposed modeling approach provides quantitative guidance for balancing signal fidelity and resource efficiency in neural recording systems.
- Enables the design of future low-power, high-accuracy neural recording systems for neurological applications.
