A high-performance 4 nV (√Hz)⁻¹ analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

Abstract

Objective Recording of local field potentials (LFPs) during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, existing recording systems lack the ability to provide artefact-free high-frequency (>100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. Approach To solve this problem, we designed and developed a novel, low-noise and versatile analog front-end (AFE) that uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, we assessed the performance of the realised AFE by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 μs). A full performance comparison between the proposed AFE and an identical AFE, equipped with an 8th order analog Bessel notch filter, was also conducted. Main results A high-performance, 4 nV (Hz)−1 AFE that is capable of recording nV-scale signals was designed in accordance with the imposed specifications. Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5–250 Hz) during stimulation. Its sensing and stimulation artefact suppression capabilities outperformed the capabilities of the AFE equipped with the Bessel notch filter. Significance The designed AFE can precisely record LFP signals, in and without the presence of either unipolar or bipolar DBS, which renders it as a functional and practical AFE architecture to be utilised in a wide range of applications and environments. This work paves the way for the development of externalized research tools for closed-loop neuromodulation that use low- and higher-frequency LFPs as control signals.

Organic Electrochemical Transistor Common‐Source Amplifier for Electrophysiological Measurements

“…The input dynamic range is defined here as the width of amplitudes within which the THD remained lower than 1%. [ 26 ] The input dynamic range was determined for each characterized amplifier using physical measurements and simulations. The gain was calculated by dividing the amplitude of the output voltage from the THD data by the amplitude of the input signal.…”

“…In hospital wards with multi‐frequency noise sources and in applications where concurrent neural sensing and electrical stimulation is required, for example in deep brain stimulation and spinal cord stimulation setups, the nonlinearity of the amplifying system must be minimized to avoid artifact coupling with the electrophysiological measurements through intermodulation. [ 26 ]…”

A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson’s Disease

“…Although external systems were able to solve the artifact rejection problem ( Rossi et al, 2007 ; Arlotti et al, 2016b ; Petkos et al, 2019 ), the size and power constrains of implantable operations make the rejection of stimulation artifact a technical implementation challenge. The stimulation artifact consists of direct components (stimulus time-locked voltage transients) and indirect components (voltage decay in the inter-pulse period) ( Zhou et al, 2019 ).…”

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings using SVD-based Adaptive Filtering

“…Several filtering techniques have been proposed for removing artifacts from LFP recordings in the literature. These include a low-pass filter [4], a notch filter [5], and band-pass filters [6]. Specifically, [4] focused on removing high-frequency artifacts generated by DBS (130 Hz) by implementing a low-pass filter with a corner frequency of 50 Hz using Butterworth coefficients.…”

“…Specifically, [4] focused on removing high-frequency artifacts generated by DBS (130 Hz) by implementing a low-pass filter with a corner frequency of 50 Hz using Butterworth coefficients. Another study [5] reported that no biomarkers for Parkinson's disease were found in the frequency band between 125 and 155 Hz and thus a high-order (8th) Chebyshev notch filter with a center frequency of 140 Hz and stopband between 125 and 155 Hz was used to suppress artifacts during DBS. A band-pass filter with a frequency band between 100 Hz and the Nyquist frequency of 211 Hz was used to eliminate all stimulation interference [6].…”