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

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

Objective. 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.Approach. 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.Main results. We applied our method to high-density multi-electrode recordings from the primate retina in anex vivosetup, 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 (R2=0.951for human 1 andR2=0.944for human 2).Significance. 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.

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

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 the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with 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 most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have 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.

A novel framework for the removal of pacing artifacts from bio-electrical recordings

BACKGROUND

Electroceuticals provide clinical solutions for a range of disorders including Parkinson's disease, cardiac arrythmias and are emerging as a potential treatment option for gastrointestinal disorders. However, pre-clinical investigations are challenged by the large stimulation artifacts registered in bio-electrical recordings.

METHOD

A generalized framework capable of isolating and suppressing stimulation artifacts with minimal intervention was developed. Stimulation artifacts with different pulse-parameters in synthetic and experimental cardiac and gastrointestinal signals were detected using a Hampel filter and reconstructed using 3 methods: i) autoregression, ii) weighted mean, and iii) linear interpolation.

RESULTS

Synthetic stimulation artifacts with amplitudes of 2 mV and 4 mV and pulse-widths of 50 ms, 100 ms, and 200 ms were successfully isolated and the artifact window size remained uninfluenced by the pulse-amplitude, but was influenced by pulse-width (e.g., the autoregression method resulted in an identical Root Mean Square Error (RMSE) of 1.64 mV for artifacts with 200 ms pulse-width and both 2 mV and 4 mV amplitudes). The performance of autoregression (RMSE = 1.45 ± 0.16 mV) and linear interpolation (RMSE = 1.22 ± 0.14 mV) methods were comparable and better than weighted mean (RMSE = 5.54 ± 0.56 mV) for synthetic data. However, for experimental recordings, artifact removal by autoregression was superior to both linear interpolation and weighted mean approaches in gastric, small intestinal and cardiac recordings.

CONCLUSIONS

A novel signal processing framework enabled efficient analysis of bio-electrical recordings with stimulation artifacts. This will allow the bio-electrical events induced by stimulation protocols to be efficiently and systematically evaluated, resulting in improved stimulation therapies.

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

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.

Advances in Neural Recording and Stimulation Integrated Circuits

In the past few decades, driven by the increasing demands in the biomedical field aiming to cure neurological diseases and improve the quality of daily lives of the patients, researchers began to take advantage of the semiconductor technology to develop miniaturized and power-efficient chips for implantable applications. The emergence of the integrated circuits for neural prosthesis improves the treatment process of epilepsy, hearing loss, retinal damage, and other neurological diseases, which brings benefits to many patients. However, considering the safety and accuracy in the neural prosthesis process, there are many research directions. In the process of chip design, designers need to carefully analyze various parameters, and investigate different design techniques. This article presents the advances in neural recording and stimulation integrated circuits, including (1) a brief introduction of the basics of neural prosthesis circuits and the repair process in the bionic neural link, (2) a systematic introduction of the basic architecture and the latest technology of neural recording and stimulation integrated circuits, (3) a summary of the key issues of neural recording and stimulation integrated circuits, and (4) a discussion about the considerations of neural recording and stimulation circuit architecture selection and a discussion of future trends. The overview would help the designers to understand the latest performances in many aspects and to meet the design requirements better.