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

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-12-08 DOI:10.1088/1741-2552/ad1385
Tao Xie, T. Foutz, M. Adamek, James R Swift, Cory S Inman, Joseph R Manns, E. Leuthardt, Jon T. Willie, Peter Brunner
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Abstract

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 9 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 9 human subjects. 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 to 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 with 94±1.47% sensitivity and 99±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.
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利用基于匹配追寻的新型伪影重建和去除方法(MPARRM)去除单脉冲电刺激伪影
目的:单脉冲电刺激(SPES)已被广泛用于检测有效连通性。然而,对神经反应的分析常常被刺激伪影所混淆。我们开发了一种新的基于匹配追踪的伪影重建和去除方法(MPARRM),能够从刺激伪影影响的电生理信号中去除伪影。方法:为了在广泛的潜在刺激伪影类型中验证MPARRM,我们进行了一个台式实验,将电极悬浮在盐水溶液中,产生110种真实世界的刺激伪影。然后,我们将生成的刺激伪影添加到真实信号(9名人类受试者在接受性语音任务中记录的立体脑电图信号)中,对组合信号应用MPARRM,并将合成的去噪信号与真实信号进行比较。在对9名受试者的同侧基底外侧杏仁核进行spe时,我们进一步将MPARRM应用于从海马体记录的伪影影响神经信号。结果:MPARRM可以在不引入频谱泄漏和时间扩散的情况下去除刺激伪影。它可以适应不同的刺激参数,并在很宽的频带范围内恢复对spe的早期响应。具体而言,在刺激开始后5 ~ 10 ms的早期反应期,我们发现去噪信号的宽带伽马功率(70 ~ 170 Hz)与地面真实信号高度相关(R=0.98±0.02,Pearson),去噪信号的宽带伽马活度以94±1.47%的灵敏度和99±1.01%的特异性忠实地反映了地面真实信号内对听觉刺激的反应。我们进一步发现,MPARRM可以揭示对同侧杏仁核刺激的海马前后轴宽带伽马活动的预期时间进展。意义:MPARRM可以在不混淆电生理信号成分的情况下忠实地去除spe伪影,特别是在反应早期。该方法有助于理解SPES的神经反应机制。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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