Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces (original) (raw)
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Neural implants that electrically stimulate neural tissue such as deep brain stimulators, cochlear implants (CI), and vagal nerve stimulators are becoming the routine treatment options for various diseases. Optimizing electrical stimulation paradigms requires closed-loop stimulation using simultaneous recordings of evoked neural activity in real time. Stimulus-evoked artifacts at the recording site are generally orders of magnitude larger than the neural signals, which challenge the interpretation of evoked neural activity. We developed a generalized artifact removal algorithm that can be applied in a variety of neural recording modalities. The procedure leverages known electrical stimulation currents to derive optimal filters that are used to predict and remove artifacts. We validated the procedure using paired recordings and electrical stimulation from sciatic nerve axons, high-rate bilateral CI stimulation, and concurrent multichannel stimulation in auditory midbrain and recordin...
A novel stimulus artifact removal technique for high-rate electrical stimulation
Journal of Neuroscience Methods, 2008
Electrical stimulus artifact corrupting electrophysiological recordings often makes the subsequent analysis of the underlying neural response difficult. This is particularly evident when investigating short-latency neural activity in response to high-rate electrical stimulation. We developed and evaluated an off-line technique for the removal of stimulus artifact from electrophysiological recordings. Pulsatile electrical stimulation was presented at rates of up to 5000 pulses/s during extracellular recordings of guinea pig auditory nerve fibers. Stimulus artifact was removed by replacing the sample points at each stimulus artifact event with values interpolated along a straight line, computed from neighbouring sample points. This technique required only that artifact events be identifiable and that the artifact duration remained less than both the inter-stimulus interval and the time course of the action potential. We have demonstrated that this computationally efficient sample-and-interpolate technique removes the stimulus artifact with minimal distortion of the action potential waveform. We suggest that this technique may have potential applications in a range of electrophysiological recording systems.
Journal of Neural Engineering, 2020
Objective. Electrocorticogram (ECoG)-based brain-computer interfaces (BCIs) are a promising platform for the restoration of motor and sensory functions to those with neurological deficits. Such bi-directional BCI operation necessitates simultaneous ECoG recording and stimulation, which is challenging given the presence of strong stimulation artifacts. This problem is exacerbated if the BCI's analog front-end operates in an ultra-low power regime, which is a basic requirement for fully implantable medical devices. In this study, we developed a novel method for the suppression of stimulation artifacts before they reach the analog front-end. Approach. Using elementary biophysical considerations, we devised an artifact suppression method that employs a weak auxiliary stimulation delivered between the primary stimulator and the recording grid. The exact location and amplitude of this auxiliary stimulating dipole were then found through a constrained optimization procedure. The performance of our method was tested in both simulations and phantom brain tissue experiments. Main results. The solution found through the optimization procedure matched the optimal canceling dipole in both simulations and experiments. Artifact suppression as large as 28.7 dB and 22.9 dB were achieved in simulations and brain phantom experiments, respectively. Significance. We developed a simple constrained optimization-based method for finding the parameters of an auxiliary stimulating dipole that yields optimal artifact suppression. Our method suppresses stimulation artifacts before they reach the analog front-end and may prevent the front-end amplifiers from saturation. Additionally, it can be used along with other artifact mitigation techniques to further reduce stimulation artifacts.
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
Fully-implantable, bi-directional brain-computer interfaces (BCIs) necessitate simultaneous cortical recording and stimulation. This is challenging since electrostimulation of cortical tissue typically causes strong artifacts that may saturate ultra-low power (ULP) analog front-ends of fullyimplantable BCIs. To address this problem, we propose an efficient hardware-based method for artifact suppression that employs an auxiliary stimulator with polarity opposite to that of the primary stimulator. The feasibility of this method was explored first in simulations, and then experimentally with brain phantom tissue and electrocorticogram (ECoG) electrode grids. We find that the canceling stimulator can reduce stimulation artifacts below the saturation limit of a typical ULP front-end, while delivering only ∼10% of the primary stimulator's voltage.
Adaptive Filters to Remove Deep Brain Stimulation Artifacts from Local Field Potentials
2019 Conference on Cognitive Computational Neuroscience, 2019
Deep Brain Stimulation (DBS) has continuously gained popularity as a symptomatic treatment in diseases such as Parkinson's Disease (PD), Essential Tremor (ET), and dystonia. For better understanding the mechanisms of DBS, a series of intraoperative Local Field Potential (LFP) recordings are acquired from patients during DBS. These recordings are vastly affected by stimulation artifacts (SAs). Despite the recent advancements in digital-and analog-based processing methods in removing SAs, a common approach in Neuroscience community is to delete an entire time interval affected by such artifact (lasting about 5 milliseconds after the onset of DBS pulse). In this paper, we propose a robust computational framework based on adaptive filtering strategy to automatically estimate the artifact induced by each individual DBS pulse, and to recover the neural response during the artifact. An estimate of the common identical artifact is obtained by fitting a B-Spline smoothing function to the average of all recordings followed by DBS pulse. The common artifact, for each individual pulse, is then fed to a Normalized Least Mean Square (NLMS) adaptive filter whose error is equal to the difference between the recorded data and the adapted artifact i.e., the recovered neural response. This framework is then confirmed using the LFP recorded from patients with PD at the level of Subthalamic Nucleus (STN). The artifact is visibly and quantifiably diminished after ~ 1.5 msec after the onset of DBS pulse. This will allow researchers to peek further into the mechanism of action and health effects of DBS. We believe that this work will broaden window of clarity will pave the way for the development of accurate bidirectional closed-loop DBS techniques.
Frontiers in Human Neuroscience
Bidirectional deep brain stimulation (DBS) platforms have enabled a surge in hours of recordings in naturalistic environments, allowing further insight into neurological and psychiatric disease states. However, high amplitude, high frequency stimulation generates artifacts that contaminate neural signals and hinder our ability to interpret the data. This is especially true in psychiatric disorders, for which high amplitude stimulation is commonly applied to deep brain structures where the native neural activity is miniscule in comparison. Here, we characterized artifact sources in recordings from a bidirectional DBS platform, the Medtronic Summit RC + S, with the goal of optimizing recording configurations to improve signal to noise ratio (SNR). Data were collected from three subjects in a clinical trial of DBS for obsessive-compulsive disorder. Stimulation was provided bilaterally to the ventral capsule/ventral striatum (VC/VS) using two independent implantable neurostimulators. We...
Journal of Neural Engineering, 2007
The clinical efficacy of high-frequency deep brain stimulation (DBS) for Parkinson's disease and other neuropsychiatric disorders likely depends on the modulation of neuronal rhythms in the target nuclei. This modulation could be effectively measured with local field potential (LFP) recordings during DBS. However, a technical drawback that prevents LFPs from being recorded from the DBS target nuclei during stimulation is the stimulus artefact. To solve this problem, we designed and developed 'FilterDBS', an electronic amplification system for artefact-free LFP recordings (in the frequency range 2-40 Hz) during DBS. After defining the estimated system requirements for LFP amplification and DBS artefact suppression, we tested the FilterDBS system by conducting experiments in vitro and in vivo in patients with advanced Parkinson's disease undergoing DBS of the subthalamic nucleus (STN). Under both experimental conditions, in vitro and in vivo, the FilterDBS system completely suppressed the DBS artefact without inducing significant spectral distortion. The FilterDBS device pioneers the development of an adaptive DBS system retroacted by LFPs and can be used in novel closed-loop brain-machine interface applications in patients with neurological disorders. 6
Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays
2016
Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis....