An impedance matching algorithm for common-mode interference removal in vagus nerve recordings.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Accurate peripheral nervous system signal recording is crucial for diagnostic bioelectronic medicine, neuromodulation, and neuroprosthetics.
• Challenges in neural signal acquisition stem from biological and instrument-related interference sources.
• Existing methods like simple subtraction are limited by impedance mismatches.

Purpose of the Study:

• To develop and validate a novel common-mode interference rejection algorithm for bipolar cuff electrodes.
• To improve the accuracy and reliability of neural signal recordings in the presence of artifacts.

Main Methods:

• Developed a common-mode interference rejection algorithm utilizing an impedance matching approach.
• Recorded two unipolar channels from bipolar cuff electrodes.
• Estimated and corrected for impedance mismatch between electrode channels.

Main Results:

• The algorithm reduced electrocardiographic (ECG) artifact interference to noise ratio (INR) by 12 dB compared to simple subtraction.
• Further reduced stimulation artifact INR by 2.4 dB and evoked electromyographic (EMG) interference by 1.3 dB.
• Successfully retained neural signals while suppressing various interference types in animal models.

Conclusions:

• The developed algorithm significantly mitigates common-mode interference from ECG, stimulation, and EMG artifacts.
• This impedance matching approach enhances neural signal quality across different neurophysiological setups and animal models.
• The method offers a robust solution for improving signal fidelity in bioelectronic applications.