Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces

Najafabadi, Mina Sadeghi; Chen, Longtu; Dutta, Kelsey; Norris, Ashley; Feng, Bin; Schnupp, Jan W. H.; Roßkothen-Kuhl, Nicole; Read, Heather L.; Escabı́, Monty A.

doi:10.3389/fnins.2020.00709

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…Artefact removal in this context may require a combination of hardware (e.g., blanking systems to prevent amplifier saturation during stimulation) and signal processing components [125], which increases the complexity of the instrumentation required and contributes to the low number of studies. Nonetheless, the variety of techniques described in this article and the recent acceleration of the field suggest that we are at a turning point in this regard and artefact rejection signal processing schemes that demonstrate a 25-40 dB rejection in the artefact are now available [126].…”

Section: Closed-loop Interfacesmentioning

confidence: 99%

Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

et al. 2022

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The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of peripheral nerve interfaces, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.

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Section: Closed-loop Interfacesmentioning

confidence: 99%

Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

et al. 2022

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“…In contrast, our approach only exploits a short time window of recordings to recover input templates via the input estimator. Hence, the input estimator functions as a computationally efficient filter over the recordings similar to the Wiener filters in [51,65]. However, further research is required to adapt this method to a real-time hardware-friendly implementation.…”

Section: Computational Efficiencymentioning

confidence: 99%

“…In template subtraction methods, the estimated artifacts are subtracted from the measurements to isolate neural activity [13,23,46,65] and identify spikes [40]. However, obtaining templates of the artifact in isolation is not always possible [51].…”

Section: Introductionmentioning

confidence: 99%

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach

Shokri,

Gogliettino,

Hottowy

et al. 2024

J. Neural Eng.

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Background: Bi-directional electronic neural interfaces, capable of both electrical recording and stimulation, communicate with the nervous system to permit precise calibration of electrical inputs by capturing the evoked neural responses. However, one significant challenge is that stimulation artifacts often mask the actual neural signals. To address this issue, we introduce a novel approach that employs dynamical control systems to detect and decipher electrically evoked neural activity despite the presence of electrical artifacts.Methods: Our proposed method leverages the unique spatiotemporal patterns of neural activity and electrical artifacts to distinguish and identify individual neural spikes. We designed distinctive dynamical models for both the stimulation artifact and each neuron observed during spontaneous neural activity. We can estimate which neurons were active by analyzing the recorded voltage responses across multiple electrodes post-stimulation. This technique also allows us to exclude signals from electrodes heavily affected by stimulation artifacts, such as the stimulating electrode itself, yet still accurately differentiate between evoked spikes and electrical artifacts.Results: We applied our method to high-density multi-electrode recordings from the primate retina in an ex vivo setup, using a grid of 512 electrodes. Through repeated electrical stimulations at varying amplitudes, we were able to construct activation curves for each neuron. The curves obtained with our method closely resembled those derived from manual spike sorting. Additionally, the stimulation thresholds we estimated strongly agreed with those determined through manual analysis, demonstrating high reliability (R^2 = 0.951 for human 1 and R^2 = 0.944 for human 2).Conclusion: Our method can effectively separate evoked neural spikes from stimulation artifacts by exploiting the distinct spatiotemporal propagation patterns captured by a dense, large-scale multi-electrode array. This technique holds promise for future applications in real-time closed-loop stimulation systems and for managing multi-channel stimulation strategies.

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“…The missing data was then interpolated by utilizing the neighboring samples that preceded and followed the contaminated period. Other solutions proposed to address the issue of DBS-artifact in LFP recordings include: Adaptive filtering [24], Linear Wiener filtering [25], template subtraction using an adaptive shape based on the Euclidean median of k-nearest neighbors [26], Weighted moving average template subtraction, in which the template is estimated as a weighted average of a limited number of neighboring pulses [27], A template-based subtraction method that utilizes past artifact samples and linear regression [28], Interpolation techniques, including Linear proposed [29], Gaussian [30], and Cubic Spline [31], A symbiotic combination of front-end and back-end template subtraction [32], Polynomial subtraction of power spectral density in the frequency domain to mitigate low-frequency distortions in LFP arising from impedance mismatch during DBS therapy [33]. Despite the effectiveness of these techniques in filtering unwanted noise, there is always a risk of over-filtering, which means that these methods may remove not only unwanted noise but also important information from the signal.…”

Section: Introductionmentioning

confidence: 99%

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

Bahador

Saha

Rezaei

et al. 2023

Preprint

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Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on how a burst of DBS pulses, usually delivered at clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with stereotype large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of the most of methods for removing DBS artifact was on the former, the artifact removal of the slow signal has not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) Normalized least mean square adaptive filtering, (2) Optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) Moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

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Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces

Cited by 7 publications

References 47 publications

Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach

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

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