LAUSR

BACKGROUND The peripheral nervous system is involved in a multitude of physiological functions. Recording neural signals provides information that can be used by diagnostic bioelectronic medicine devices, closed-loop neuromodulation therapies and other neuroprosthetic applications. The ability to accurately record these signals is challenging, due to the presence of various biological and instrument-related interference sources. NEW METHOD We developed a common-mode interference rejection algorithm based on an impedance matching approach for bipolar cuff electrodes. Two unipolar channels were recorded from the two electrode contacts of a bipolar cuff. The impedance mismatch was estimated and used to correct one of the two channels. RESULTS When applied to electrocardiographic (ECG) artifacts collected from three mice using CorTec electrodes, the algorithm reduced the interference to noise ratio (INR) over simple subtraction by 12 dB on average. The algorithm also reduced the INR of stimulation artifacts in recordings from three rats collected using flexible electrodes by an additional 2.4 dB. In the same experiments evoked electromyographic (EMG) interference was suppressed by 1.3 dB. COMPARISON WITH EXISTING METHODS SIMPL: e subtraction is the common approach for reducing common-mode interference in bipolar recordings, however impedance mismatches that exist or emerge compromise its efficiency. CONCLUSIONS The algorithm significantly reduced the common-mode interference from ECG artifacts, stimulation artifacts, and evoked EMG interference, while retaining neural signals, in two animal models and two recording setups. This approach can be used in a variety of different neurophysiological setups to remove common-mode interference from a variety of sources.