Moving average template subtraction to remove stimulation artefacts in EEGs and LFPs recorded during deep brain stimulation

Deep Brain Stimulator (DBS) Artifact in the EEG of a Pediatric Patient

We report the first case of deep brain stimulator (DBS) artifact in the EEG of a pediatric patient. Our case is a 7-year-old male with bilateral globus pallidus interna (GPi) DBS for whom the EEG recorded a rhythmic 7.5 Hz theta activity on EEG related to DBS artifact. This artifact was also appreciated as a monochromatic invariable frequency band over 7.5 Hz on density spectral array (DSA). This rhythmic artifact may mimic an ictal pattern and should be recognized as artifact in order to avoid unnecessary treatment with anti-seizure medications (ASM).

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

BACKGROUND

Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis.

NEW METHOD

In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered.

RESULT

SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates.

COMPARISON WITH EXISTING METHODS

SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering.

CONCLUSION

The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)

Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

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.

Objective evaluation of stimulation artefact removal techniques in the context of neural spike sorting

Objective - We present a framework to objectively test and compare stimulation artefact removal techniques in the context of neural spike sorting. Approach - To this end, we used realistic hybrid ground-truth spiking data, with superimposed artefacts from in vivo recordings. We used the framework to evaluate and compare several techniques: blanking, template subtraction by averaging, linear regression, and a multi-channel Wiener filter (MWF). Main results - Our study demonstrates that blanking and template subtraction result in a poorer spike sorting performance than linear regression and MWF, while the latter two perform similarly. Finally, to validate the conclusions found from the hybrid evaluation framework, we also performed a qualitative analysis on in vivo recordings without artificial manipulations. Significance - Our framework allows direct quantification of the impact of the residual artefact on the spike sorting accuracy, thereby allowing for a more objective and more relevant comparison compared to indirect signal quality metrics that are estimated from the signal statistics. Furthermore, the availability of a ground truth in the form of single-unit spiking activity also facilitates a better estimation of such signal quality metrics.

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Advances in therapeutic neuromodulation devices have enabled concurrent stimulation and electrophysiology in the central nervous system. However, stimulation artifacts often obscure the sensed underlying neural activity. Here, we develop a method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker are used for validation. Performance is found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: (1) it is superior in signal recovery; (2) it is easily adaptable to several neurostimulation paradigms; and (3) it has low complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.

Removal of the Sinusoidal Transorbital Alternating Current Stimulation Artifact From Simultaneous EEG Recordings: Effects of Simple Moving Average Parameters

Alternating current stimulation is a promising method for the study and treatment of various visual neurological dysfunctions as well as progressive understanding of the healthy brain. Unfortunately, due to the current stimulation artifact, problems remain in the context of analysis of the electroencephalography (EEG) signal recorded during ongoing stimulation. To address this problem, we propose the use of a simple moving average subtraction as a method for artifact elimination. This method involves the creation of a template of the stimulation artifact from EEG signal recorded during non-invasive electrical stimulation with a sinusoidal alternating current. The present report describes results of the effects of a simple moving average filtration that varies based on averaging parameters; in particular, we varied the number of sinusoidal periods per segment of the recorded signal and the number of segments used to construct an artifact template. Given the ongoing lack of a mathematical model that allows for the prediction of the “hidden” EEG signal with the alternating current stimulation artifact, we propose performing an earlier simulation that is based on the addition of artificial stimulation artifact to the known EEG signal. This solution allows for the optimization of filtering parameters with detailed knowledge about the accuracy of artifact removal. The algorithm, designed in the MATLAB environment, has been tested on data recorded from two volunteers subjected to sinusoidal transorbital alternating current stimulation. Analysis of the percentage difference between the original and filtered signal in time and frequency domain highlights the advantage of 1-period filtration.

The comparative performance of DBS artefact rejection methods for MEG recordings

Deep brain stimulation (DBS) can be a very efficient treatment option for movement disorders and psychiatric diseases. To better understand DBS mechanisms, brain activity can be recorded using magnetoencephalography (MEG) with the stimulator turned on. However, DBS produces large artefacts compromising MEG data quality due to both the applied current and the movement of wires connecting the stimulator with the electrode. To filter out these artefacts, several methods to suppress the DBS artefact have been proposed in the literature. A comparative study evaluating each method’s effectiveness, however, is missing so far.

In this study, we evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system: (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation (tSSS). In the sensor space, the largest increase in signal-to-noise (SNR) ratio was achieved by ICA-MI, while the best correspondence in terms of source activations was obtained by tSSS. LCMV beamforming alone was not sufficient to suppress the DBS-induced artefacts.

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Phantom MEG measurement with Elekta Neuromag and CTF MEG system with DBS.

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Systematic comparison of cleaning algorithms to remove DBS artefact from MEG data.

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Sensor level ICA-MI yielded the best results.

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Source level: tSSS provided the best correspondence to recording without DBS.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

OBJECTIVE

Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available.

APPROACH

We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings.

MAIN RESULTS

We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation.

SIGNIFICANCE

Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

Vibrotactile piezoelectric stimulation system with precise and versatile timing control for somatosensory research

BACKGROUND

Tactile stimulations systems are useful for studying the somatosensory system in children because they are innocuous and safe. Stimulators based on piezoelectric actuator are useful, but there is still a need for such systems capable of providing accurate and versatile control of timing and pattern of activation.

NEW METHOD

We have implemented a vibrotactile stimulating system useful for behavioral and electroencephalography (EEG) and magnetoencephalography (MEG) research. Our design goal was to create a system capable of providing up to five independently controlled mechanical stimulations with precise timing. We developed a Graphic User Interface (GUI) in LabVIEW, which controls a commercially available piezoelectric braille stimulator using an Arduino based controller. We made a customized braille stimulator based on the Metec Braille device.

RESULTS

Our system can control up to five tactile stimulators with independent timing control and negligible errors. Although it can be directly used for behavioral and EEG research, the piezoelectric stimulators in our system generate stimulus artifacts that interfere with MEG recordings. A moving averaging subtraction algorithm we developed can remove the artifact. The stimulator can be used to measure somatic evoked magnetic fields from the somatosensory cortex of a child without the artifact.

COMPARISON TO EXISTING METHODS

Our system provides an accurate independent control of one or more piezoelectric actuators using a GUI-based easy-to-control programming approach based on recent advances in embedded systems and software. Versatility and precise stimulation timing distinguish our system compared to existing somatic stimulators.

CONCLUSIONS

Our system may be useful for somatic research.

Removing deep brain stimulation artifacts from the electroencephalogram: Issues, recommendations and an open-source toolbox

A major question for deep brain stimulation (DBS) research is understanding how DBS of one target area modulates activity in different parts of the brain. EEG gives privileged access to brain dynamics, but its use with implanted patients is limited since DBS adds significant high-amplitude electrical artifacts that can completely obscure neural activity measured using EEG. Here, we systematically review and discuss the methods available for removing DBS artifacts. These include simple techniques such as oversampling, antialiasing analog filtering and digital low-pass filtering, which are necessary but typically not sufficient to fully remove DBS artifacts when each is used in isolation. We also cover more advanced methods, including techniques tracking outliers in the frequency-domain, which can be effective, but are rarely used. The reason for that is twofold: First, it requires advanced skills in signal processing since no user friendly tool for removing DBS artifacts is currently available. Second, it involves fine-tuning to avoid over-aggressive filtering. We highlight an open-source toolbox incorporating most artifact removal methods, allowing users to combine different strategies.

Animal models of early-stage Parkinson's disease and acute dopamine deficiency to study compensatory neurodegenerative mechanisms

Parkinson's disease is a common neurodegenerative disease characterized by a widely variety of motor and non-motor symptoms. While the motor deficits are only visible following a severe dopamine depletion, neurodegenerative process and some non-motor symptoms are manifested years before the motor deficits. Importantly, chronic degeneration of dopaminergic neurons leads to the development of compensatory mechanisms that play roles in the progression of the disease and the response to anti-parkinsonian therapies. The identification of these mechanisms will be of great importance for improving our understanding of factors with important contributions to the disease course and the underlying adaptive process. To date, most of the data obtained from animal models reflect the late, chronic, dopamine-depleted states, when compensatory mechanisms have already been established. Thus, adequate animal models with which researchers are able to dissect early- and late-phase mechanisms are necessary. Here, we reviewed the literature related to animal models of early-stage PD and pharmacological treatments capable of inducing acute dopamine impairments and/or depletion, such as reserpine, haloperidol and tetrodotoxin. We highlighted the advantages, limitations and the future prospective uses of these models, as well as their applications in the identification of novel agents for treating this neurological disorder.