Differential Neural Interface Circuit for Common-Mode Noise Rejection
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Solution Overview
Problem
Conventional neural interface systems face challenges in effectively suppressing noise, particularly common-mode system noise, due to the use of single-ended electrode inputs with uncorrelated reference inputs, leading to reduced signal-to-noise ratio and increased noise coupling.
Innovation Solution
The system employs a fully differential low-noise amplifier at the input stage, utilizing on-chip generated common references and integrating analog-to-digital converters within each neural interface element, along with a 2D array structure for uniform signal routing and testing, enabling true differential reading and reducing noise by digitizing reference signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-ended electrode inputs with uncorrelated reference inputs are used, then device complexity is reduced, but signal-to-noise ratio deteriorates due to increased noise coupling
Solution Approach 1:
The patent converts the harmful common-mode noise into a beneficial signal by using fully differential amplification. The uncorrelated reference inputs that previously caused noise coupling are now used to create correlated reference signals that cancel out common-mode noise, transforming the noise problem into a noise-rejection solution.
Solution Approach 2:
The patent changes the fundamental parameter of electrode input configuration from single-ended to fully differential. This parameter change enables the system to reject common-mode noise while maintaining device complexity at an acceptable level through integrated circuit implementation.
2Measurement precision
If fully differential low-noise amplifiers with on-chip generated common references are used, then signal-to-noise ratio is improved by suppressing common-mode noise, but device complexity increases
Solution Approach 1:
The patent merges multiple functions into the fully differential amplifier architecture: noise rejection, signal amplification, and reference generation are all integrated into a single circuit block. This consolidation improves signal-to-noise ratio while minimizing the increase in device complexity through functional integration.
Solution Approach 2:
The on-chip generated common references provide self-service by automatically creating correlated reference signals that cancel common-mode noise without requiring external intervention. The system generates its own noise-rejection mechanism, improving measurement precision while keeping the device self-contained.
3Object-affected harmful factors
If analog-to-digital converters are integrated within each neural interface element, then system noise is reduced by digitizing reference signals, but manufacturing precision requirements increase
Solution Approach 1:
The patent segments the neural interface system into discrete elements, each with its own integrated analog-to-digital converter. This segmentation allows independent optimization of each element and reduces the cumulative noise from reference signals, while standardization of the segmented modules helps manage manufacturing precision requirements.
Solution Approach 2:
The patent replaces analog reference signal distribution with digital reference signal distribution through integrated analog-to-digital converters. This substitution eliminates analog noise coupling in the reference signal path, reducing system noise while the digital domain provides inherent robustness to manufacturing variations.
Data Source
AI summary
The system (e.g., neural interface system) can include: one or more recording modules and/or one or more stimulus modules. In an example, the system can include an electrical recording module and an optical stimulus module. In an example, the electrical recording module can include: a neural interface module, a digital controller module, an analog drive module, and/or an electrode multiplexer. In a specific example, the optical stimulus module can include: a LED driver IC and set of LEDs, where the LED driver IC provides required power and control signals to run the set of LEDs and set of LEDs apply desired optical stimulus to a set of neurons. In variants, the system can function to read signals from neurons (e.g., reading electrical signals via electrodes) and/or transmit signals to neurons (e.g., transmitting electrical signals via electrodes and/or transmitting light signals via LEDs).