Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

Comparison of electrical microstimulation artifact removal methods for high-channel-count prostheses

BACKGROUND

Neuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as blindness, paralysis, and other movement and psychiatric disorders. Recordings are often made simultaneously with stimulation, allowing the monitoring of neural signals and closed-loop control of devices. However, stimulation-evoked artifacts may obscure neural activity, particularly when stimulation and recording sites are nearby. Several methods have been developed to remove stimulation artifacts, but it remains challenging to validate and compare these methods because the 'ground-truth' of the neuronal signals may be contaminated by artifacts.

NEW METHOD

Here, we delivered stimulation to the visual cortex via a high-channel-count prosthesis while recording neuronal activity and stimulation artifacts. We quantified the waveforms and temporal properties of stimulation artifacts from the cortical visual prosthesis (CVP) and used them to build a dataset, in which we simulated the neuronal activity and the stimulation artifacts. We illustrate how to use the simulated data to evaluate the performance of six software-based artifact removal methods (Template subtraction, Linear interpolation, Polynomial fitting, Exponential fitting, SALPA and ERAASR) in a CVP application scenario.

RESULTS

We here focused on stimulation artifacts caused by electrical stimulation through a high-channel-count cortical prosthesis device. We find that the Polynomial fitting and Exponential fitting methods outperform the other methods in recovering spikes and multi-unit activity. Linear interpolation and Template subtraction recovered the local-field potentials.

CONCLUSION

Polynomial fitting and Exponential fitting provided a good trade-off between the quality of the recovery of spikes and multi-unit activity (MUA) and the computational complexity for a cortical prosthesis.

Data processing techniques impact quantification of cortico-cortical evoked potentials

BACKGROUND

Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study.

NEW METHOD

We systematically compare how common average re-referencing and filtering CCEP data impacts quantification.

RESULTS

We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise.

COMPARISON WITH EXISTING METHODS

These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods.

CONCLUSIONS

We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.

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.

Electrical stimulation induced self-related auditory hallucinations correlate with oscillatory power change in the default mode network

Self-related information is crucial in our daily lives, which has led to the proposal that there is a specific brain mechanism for processing it. Neuroimaging studies have consistently demonstrated that the default mode network (DMN) is strongly associated with the representation and processing of self-related information. However, the precise relationship between DMN activity and self-related information, particularly in terms of neural oscillations, remains largely unknown. We electrically stimulated the superior temporal and fusiform areas, using stereo-electroencephalography to investigate neural oscillations associated with elicited self-related auditory hallucinations. Twenty-two instances of auditory hallucinations were recorded and categorized into self-related and other-related conditions. Comparing oscillatory power changes within the DMN between self-related and other-related auditory hallucinations, we discovered that self-related hallucinations are associated with significantly stronger positive power changes in both alpha and gamma bands compared to other-related hallucinations. To ensure the validity of our findings, we conducted controlled analyses for factors of familiarity and clarity, which revealed that the observed effects within the DMN remain independent of these factors. These results underscore the significance of the functional role of the DMN during the processing of self-related auditory hallucinations and shed light on the relationship between self-related perception and neural oscillatory activity.

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.

On the Stimulation Artifact Reduction during Electrophysiological Recording of Compound Nerve Action Potentials. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-5. [PMID: 38083005 DOI: 10.1109/embc40787.2023.10341179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this work, we present a model to better understand the effect of the selected amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. Simulation results show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration are optimized. We experimentally demonstrate the effects in three different settings, in-vivo and in-vitro.

Imaging the effective networks associated with cortical function through intracranial high-frequency stimulation

Direct electrical stimulation (DES) is considered to be the gold standard for mapping cortical function. A careful mapping of the eloquent cortex is key to successful resective or ablative surgeries, with a minimal postoperative deficit, for treatment of drug‐resistant epilepsy. There is accumulating evidence suggesting that not only local, but also remote activations play an equally important role in evoking clinical effects. By introducing a new intracranial stimulation paradigm and signal analysis methodology allowing to disambiguate EEG responses from stimulation artifacts we highlight the spatial extent of the networks associated with clinical effects. Our study includes 26 patients that underwent stereoelectroencephalographic investigations for drug‐resistant epilepsy, having 337 depth electrodes with 4,351 contacts sampling most brain structures. The routine high‐frequency electrical stimulation protocol for eloquent cortex mapping was altered in a subtle way, by alternating the polarity of the biphasic pulses in a train, causing the splitting the spectral lines of the artifactual components, exposing the underlying tissue response. By performing a frequency‐domain analysis of the EEG responses during DES we were able to capture remote activations and highlight the effect's network. By using standard intersubject averaging and a fine granularity HCP‐MMP parcellation, we were able to create local and distant connectivity maps for 614 stimulations evoking specific clinical effects. The clinical value of such maps is not only for a better understanding of the extent of the effects' networks guiding the invasive exploration, but also for understanding the spatial patterns of seizure propagation given the timeline of the seizure semiology.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

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.

Energy-Efficient Integrated Circuit Solutions Toward Miniaturized Closed-Loop Neural Interface Systems

Miniaturized implantable devices play a crucial role in neural interfaces by monitoring and modulating neural activities on the peripheral and central nervous systems. Research efforts toward a compact wireless closed-loop system stimulating the nerve automatically according to the user's condition have been maintained. These systems have several advantages over open-loop stimulation systems such as reduction in both power consumption and side effects of continuous stimulation. Furthermore, a compact and wireless device consuming low energy alleviates foreign body reactions and risk of frequent surgical operations. Unfortunately, however, the miniaturized closed-loop neural interface system induces several hardware design challenges such as neural activity recording with severe stimulation artifact, real-time stimulation artifact removal, and energy-efficient wireless power delivery. Here, we will review recent approaches toward the miniaturized closed-loop neural interface system with integrated circuit (IC) techniques.

Investigating Nuisance Effects Induced in EEG During tACS Application

Transcranial alternating-current stimulation (tACS) in the frequency range of 1-100 Hz has come to be used routinely in electroencephalogram (EEG) studies of brain function through entrainment of neuronal oscillations. It turned out, however, to be highly non-trivial to remove the strong stimulation signal, including its harmonic and non-harmonic distortions, as well as various induced higher-order artifacts from the EEG data recorded during the stimulation. In this paper, we discuss some of the problems encountered and present methodological approaches aimed at overcoming them. To illustrate the mechanisms of artifact induction and the proposed removal strategies, we use data obtained with the help of a schematic demonstrator setup as well as human-subject data.

Intraoperative Characterization of Subthalamic Nucleus-to-Cortex Evoked Potentials in Parkinson's Disease Deep Brain Stimulation

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a clinically effective tool for treating medically refractory Parkinson's disease (PD), but its neural mechanisms remain debated. Previous work has demonstrated that STN DBS results in evoked potentials (EPs) in the primary motor cortex (M1), suggesting that modulation of cortical physiology may be involved in its therapeutic effects. Due to technical challenges presented by high-amplitude DBS artifacts, these EPs are often measured in response to low-frequency stimulation, which is generally ineffective at PD symptom management. This study aims to characterize STN-to-cortex EPs seen during clinically relevant high-frequency STN DBS for PD. Intraoperatively, we applied STN DBS to 6 PD patients while recording electrocorticography (ECoG) from an electrode strip over the ipsilateral central sulcus. Using recently published techniques, we removed large stimulation artifacts to enable quantification of STN-to-cortex EPs. Two cortical EPs were observed - one synchronized with DBS onset and persisting during ongoing stimulation, and one immediately following DBS offset, here termed the "start" and the "end" EPs respectively. The start EP is, to our knowledge, the first long-latency cortical EP reported during ongoing high-frequency DBS. The start and end EPs differ in magnitude (p < 0.05) and latency (p < 0.001), and the end, but not the start, EP magnitude has a significant relationship (p < 0.001, adjusted for random effects of subject) to ongoing high gamma (80-150 Hz) power during the EP. These contrasts may suggest mechanistic or circuit differences in EP production during the two time periods. This represents a potential framework for relating DBS clinical efficacy to the effects of a variety of stimulation parameters on EPs.