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Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation. 用灵活的微皮层电图阵列记录大鼠单细胞活动,为全身导航解码。
Pub Date : 2024-08-05 DOI: 10.1088/1741-2552/ad618c
Yu Tung Lo, Lei Jiang, Ben Woodington, Sagnik Middya, Marcel Braendlein, Jordan Lewis William Lam, Mervyn Jun Rui Lim, Vincent Yew Poh Ng, Jai Prashanth Rao, Derrick Wei Shih Chan, Beng Ti Ang

Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

目的:微皮质图(μECoG)阵列能够记录大脑皮层表面的神经活动,而无需穿透大脑实质。由于电极尺寸较小,以前的研究已经证明可以从皮层表面检测到单个尖峰,而且很可能来自新皮层的第一层神经元。在这里,我们测试了利用μECoG 阵列通过分离的单个单元活动解码大鼠在开阔地导航过程中的身体位置的能力。方法:将μECoG 阵列长期植入 Wistar 大鼠的初级运动皮层(M1),并在开阔地围栏中对清醒、有行为的大鼠进行神经记录。信号在 300 到 3000 Hz 之间经过带通滤波。根据定义的标准(包括波形形态和折射周期)识别阈值交叉尖峰并将其分类为不同的单元。身体位置来自视频记录。我们使用梯度提升机器根据之前 100 毫秒的尖峰数据预测身体位置,并使用相关分析来阐明位置与尖峰模式之间的关系:在μECoG的长期记录过程中,可以提取单个单位的尖峰数据,并从这些尖峰数据中解码出空间位置,在x维和y维(归一化范围从0到1)上的预测平均绝对误差分别为0.135和0.090,皮尔逊r分别为0.607和0.意义:μECoG 可以检测到可能来自皮层浅层神经元的单个单元活动,是皮层内阵列的一种很有前途的替代方法,它还具有可扩展性的优点,能以最小的增量风险覆盖大面积皮层。应在人类身上开展更多与将其用作脑机接口有关的研究。
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引用次数: 0
In vivoand dosimetric investigation on electrical vestibular stimulation with frequency- and amplitude-modulated currents. 使用频率和振幅调制电流进行前庭电刺激的体内和剂量学研究。
Pub Date : 2024-08-02 DOI: 10.1088/1741-2552/ad658f
Janita Nissi, Otto Kangasmaa, Juhani Kataja, Nicolas Bouisset, Ilkka Laakso

Objective. Normal function of the vestibular system can be disturbed using a noninvasive technique called electrical vestibular stimulation (EVS), which alters a person's sense of balance and causes false sensations of movement. EVS has been widely used to study the function of the vestibular system, and it has recently gained interest as a therapeutic tool to improve postural stability and help those suffering from vestibular dysfunction. Yet, understanding of how EVS stimulates the vestibular system, the current intensity needed to produce an effect and the frequencies at which it occurs have remained unclear.Approach. The effect of EVS on postural sway was examined in five participants using sinusoidal alternating current with time-varying amplitude from 0 to 1.5 mA and frequency from 0.1 to 10 Hz for three electrode configurations. Dosimetry of the current flow inside the head was conducted using anatomically realistic computational models created individually for each subject based on magnetic resonance imaging data. An estimate for the minimal field strength capable of affecting the vestibular system was calculated with the finite element method.Main results. Bipolar EVS at frequencies up to 10 Hz caused harmonic full-body swaying, and the frequency of the sway was the same as that of the stimulation current. The size of the sway was amplified by increasing the current intensity. Dosimetry modeling indicated that, for 0.2 mA current, the average electric field strength in the vestibular system was approximately 10-30 mV m-1, depending on the electrode montage. The size of the measured postural sway was proportional to the montage-specific electric field strength in the vestibular system.Significance. The results provide insight to EVS's working mechanisms and improve its potential as a tool to study the sense of balance.

目的:前庭系统的正常功能可以通过一种名为 "前庭电刺激"(EVS)的非侵入性技术受到干扰,这种技术会改变人的平衡感,使人产生错误的运动感觉。EVS 已被广泛用于研究前庭系统的功能,最近它作为一种治疗工具,在改善姿势稳定性和帮助前庭功能障碍患者方面受到了关注。然而,人们对 EVS 如何刺激前庭系统、产生效果所需的电流强度和频率仍不清楚:研究人员使用正弦交流电对五名参与者的姿势摇摆进行了检测,在三种电极配置下,电流振幅从 0 到 1.5~mA 不等,频率从 0.1 到 10~Hz 不等。根据磁共振成像数据为每个受试者分别创建了符合解剖学实际的计算模型,对头部内部的电流进行了剂量测定。利用有限元方法计算出了能够影响前庭系统的最小场强:频率高达 10~Hz 的双极 EVS 可引起谐波全身摇摆,摇摆的频率与刺激电流的频率相同。摇摆的幅度随着电流强度的增加而扩大。剂量测定模型显示,在 0.2~mA 电流下,前庭系统中的平均电场强度约为 10-30~mV/m (取决于电极组合)。测得的姿势摇摆的大小与前庭系统中特定单体的电场强度成正比:研究结果有助于深入了解 EVS 的工作机制,提高其作为平衡感研究工具的潜力。
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引用次数: 0
A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces. 基于模型的大脑开关,通过周期性运动图像调制实现异步脑机接口。
Pub Date : 2024-08-01 DOI: 10.1088/1741-2552/ad6595
Jianjun Meng, Songwei Li, Guangye Li, Ruijie Luo, Xinjun Sheng, Xiangyang Zhu

Objective.Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.Approach.Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.Main results.Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.Significance.This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.

目的:脑开关为异步脑机接口(aBCI)提供了一种切实可行的解决方案,它可以在没有预编程结构的情况下解码用户意图。然而,大多数基于脑电图(EEG)信号的脑开关具有较高的误判率(FPR),导致实用性较低。本研究旨在改进大脑开关的操作模式和实用性:在此,我们提出了一种基于虚拟物理模型的新型大脑开关,它利用了周期性主动调制。方法:在此,我们提出了一种新颖的基于虚拟物理模型的大脑开关,它利用了周期性主动调制,并提出了一个优化问题,即在所需 FPR 的条件下尽量减少触发时间,并根据模型获得了数值和分析近似解:主要结果:我们的基于运动想象(MI)的大脑开关可以达到 0.8FP/h FPR,中位触发时间为 58 秒。我们评估了在线设备控制中的脑开关,其平均 FPR 大大优于文献中的传统脑开关。我们利用通用空间模式(CSP)和优化方法进一步改进了拟议的脑开关。基于 MI-CSP 的脑开关的平均 FPR 为 0.3 FPs/小时,平均触发时间缩短至 21.6 秒:这项研究提供了一种新方法,可将大脑开关的 FPR 显著降低到 1 FPs/hour 以下,不到其他内源方法 FPR 的 10%(降低了一个数量级以上),而且反应时间(RT)与最先进的方法相当。与目前的非侵入式异步生物识别(BCI)相比,这是一项重大进步,将为生物识别(BCI)的临床应用开辟广泛的途径。
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引用次数: 0
Auditory nerve fiber excitability for alternative electrode placement in the obstructed human cochlea: electrode insertion in scala vestibuli versus scala tympani. 听觉神经纤维兴奋性在阻塞性人类耳蜗中的替代电极位置:电极插入前庭与鼓室。
Pub Date : 2024-08-01 DOI: 10.1088/1741-2552/ad6597
Andreas Fellner, Cornelia Wenger, Amirreza Heshmat, Frank Rattay

Objective. The cochlear implant (CI) belongs to the most successful neuro-prostheses. Traditionally, the stimulating electrode arrays are inserted into the scala tympani (ST), the lower cochlear cavity, which enables simple surgical access. However, often deep insertion is blocked, e.g. by ossification, and the auditory nerve fibers (ANFs) of lower frequency regions cannot be stimulated causing severe restrictions in speech understanding. As an alternative, the CI can be inserted into the scala vestibuli (SV), the other upper cochlear cavity.Approach. In this computational study, the excitability of 25 ANFs are compared for stimulation with ST and SV implants. We employed a 3-dimensional realistic human cochlear model with lateral wall electrodes based on aμ-CT dataset and manually traced fibers. A finite element approach in combination with a compartment model of a spiral ganglion cell was used to simulate monophasic stimulation with anodic (ANO) and cathodic (CAT) pulses of 50μs.Main results. ANO thresholds are lower in ST (mean/std =μ/σ= 189/55μA) stimulation compared to SV (μ/σ= 323/119μA) stimulation. Contrary, CAT thresholds are higher for the ST array (μ/σ= 165/42μA) compared to the SV array (μ/σ= 122/46μA). The threshold amplitude depends on the specific fiber-electrode spatial relationship, such as lateral distance from the cochlear axis, the angle between electrode and target ANF, and the curvature of the peripheral process. For CAT stimulation the SV electrodes show a higher selectivity leading to less cross-stimulation of additional fibers from different cochlear areas.Significance. We present a first simulation study with a human cochlear model that investigates an additional CI placement into the SV and its impact on the excitation behavior. Results predict comparable outcomes to ST electrodes which confirms that SV implantation might be an alternative for patients with a highly obstructed ST.

目的:人工耳蜗(CI)是最成功的神经假体之一。传统上,刺激电极阵列被植入鼓室(ST),即耳蜗的下腔,这使得手术操作非常简单。然而,由于骨化等原因,深部插入往往受阻,低频区域的听神经纤维(ANFs)无法受到刺激,导致言语理解能力严重受限。作为替代方案,CI 可插入前庭大部(SV),即另一个上耳蜗腔:在这项计算研究中,我们比较了使用 ST 和 SV 植入体进行刺激时 25 个 ANF 的兴奋性。我们根据 µ-CT 数据集和人工追踪的纤维,采用带有侧壁电极的三维真实人体耳蜗模型。我们采用有限元方法,结合螺旋神经节细胞的分室模型,模拟了 50 µs 的阳极(ANO)和阴极(CAT)脉冲单相刺激:主要结果:与 SV(µ/σ = 323/119 µA)刺激相比,ST(mean/std = µ/σ = 189/55 µA)刺激的 ANO 阈值较低。相反,ST 阵列的 CAT 阈值(µ/σ = 165/42 µA)高于 SV 阵列(µ/σ = 122/46 µA)。阈值振幅取决于特定的纤维-电极空间关系,如与耳蜗轴线的横向距离、电极与目标 ANF 之间的角度以及外周过程的曲率。对于 CAT 刺激,SV 电极显示出更高的选择性,从而减少了对来自不同耳蜗区域的其他纤维的交叉刺激:我们首次使用人体耳蜗模型进行了模拟研究,调查了将额外的 CI 置入 SV 电极及其对激励行为的影响。研究结果预测了与 ST 电极相似的结果,这证实了 SV 植入可能是 ST 高度阻塞患者的一种选择。
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引用次数: 0
Single-trial movement intention detection estimation in patients with Parkinson's disease: a movement-related cortical potential study. 帕金森病患者的单次运动意向检测估计:运动相关皮层电位研究。
Pub Date : 2024-08-01 DOI: 10.1088/1741-2552/ad6189
Mads Jochumsen, Kathrin Battefeld Poulsen, Sascha Lan Sørensen, Cecilie Sørenbye Sulkjær, Frida Krogh Corydon, Laura Sølvberg Strauss, Julie Billingsø Roos

Objectives. Parkinson patients often suffer from motor impairments such as tremor and freezing of movement that can be difficult to treat. To unfreeze movement, it has been suggested to provide sensory stimuli. To avoid constant stimulation, episodes with freezing of movement needs to be detected which is a challenge. This can potentially be obtained using a brain-computer interface (BCI) based on movement-related cortical potentials (MRCPs) that are observed in association with the intention to move. The objective in this study was to detect MRCPs from single-trial EEG.Approach. Nine Parkinson patients executed 100 wrist movements and 100 ankle movements while continuous EEG and EMG were recorded. The experiment was repeated in two sessions on separate days. Using temporal, spectral and template matching features, a random forest (RF), linear discriminant analysis, and k-nearest neighbours (kNN) classifier were constructed in offline analysis to discriminate between epochs containing movement-related or idle brain activity to provide an estimation of the performance of a BCI. Three classification scenarios were tested: 1) within-session (using training and testing data from the same session and participant), between-session (using data from the same participant from session one for training and session two for testing), and across-participant (using data from all participants except one for training and testing on the remaining participant).Main results. The within-session classification scenario was associated with the highest classification accuracies which were in the range of 88%-89% with a similar performance across sessions. The performance dropped to 69%-75% and 70%-75% for the between-session and across-participant classification scenario, respectively. The highest classification accuracies were obtained for the RF and kNN classifiers.Significance. The results indicate that it is possible to detect movement intentions in individuals with Parkinson's disease such that they can operate a BCI which may control the delivery of sensory stimuli to unfreeze movement.

目的:帕金森病患者通常会出现震颤和运动冻结等运动障碍,治疗起来十分困难。为了解除运动冻结,有人建议提供感官刺激。为避免持续刺激,需要检测运动冻结的发作,这是一项挑战。这有可能通过脑机接口(BCI)来实现,该接口基于与运动相关的皮层电位(MRCPs),可观察到与运动意图相关的皮层电位。本研究的目的是从单次脑电图中检测 MRCP:九名帕金森患者分别做了 100 次手腕运动和 100 次脚踝运动,同时连续记录脑电图和肌电图。实验在不同的日子分两次重复进行。在离线分析中,利用时间、频谱和模板匹配特征,构建了随机森林、线性判别分析和 k-nearest neighbours 分类器,以区分包含运动相关或闲置大脑活动的时序,从而提供对 BCI 性能的估计。测试了三种分类情况:1)会话内(使用来自同一会话和参与者的训练和测试数据)、会话间(使用来自同一参与者的数据,第一会话用于训练,第二会话用于测试)和跨参与者(使用来自所有参与者的数据,只有一名参与者除外,用于训练,其余参与者用于测试):会话内分类方案的分类准确率最高,达到 88%-89%,跨会话分类方案的分类准确率相似。会话间分类和跨参与者分类的准确率分别降至 69-75% 和 70-75%。随机森林和 k 近邻分类器的分类准确率最高:结果表明,检测帕金森病患者的运动意图是可能的,这样他们就可以操作 BCI,从而控制感官刺激的传递以解冻运动。
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引用次数: 0
Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality. 大鼠运动皮层脑电极界面的空间转录组学及其与记录质量的关系
Pub Date : 2024-07-31 DOI: 10.1088/1741-2552/ad5936
Quentin Whitsitt, Akash Saxena, Bella Patel, Blake M Evans, Bradley Hunt, Erin K Purcell

Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state.Objective.To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality.Approach.For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant.Main results. Results from rat motor cortex within 300µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6-7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes.Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.

研究脑内植入电极的异物反应是未来神经义肢和实验电生理学发展的一个重要研究领域。电极植入大脑后,小胶质细胞活化、反应性星形胶质细胞增生和神经元细胞死亡会使电极周围的环境与平衡状态发生显著变化。为了揭示可能影响设备功能和寿命的生理变化,我们采用了空间转录组学来识别电极植入引起的基因表达变化,并将这种不同的基因表达与神经胶质细胞反应性、神经元损失和电生理记录质量等传统指标进行比较。在这些实验中,大鼠被长期植入密歇根式功能性微电极阵列,在六周的时间内进行电生理记录(多单位活动、局部场电位)。然后将每个电极周围的脑组织冷冻切片装片,进行空间转录组学处理。对组织进行神经元和星形胶质细胞免疫标记,既为空间转录组学提供了空间参考,也为定量测量每个植入物周围的胶质纤维酸性蛋白(GFAP)和神经元核(NeuN)免疫标记提供了依据。植入后 24 小时、1 周和 6 周时,植入电极周围 300 微米范围内大鼠运动皮层的研究结果显示,植入与未植入组织切片之间存在多达 553 个显著差异表达 (DE) 基因。对重要差异表达基因的回归确定了与组织学和电生理学指标关系最密切的 6-7 个基因,揭示了记录质量和组织对植入电极反应的潜在候选生物标记物 .
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引用次数: 0
Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach. 实现无校准运动图像脑机接口:基于 VGG 的卷积神经网络和 WGAN 方法。
Pub Date : 2024-07-31 DOI: 10.1088/1741-2552/ad6598
A G Habashi, Ahmed M Azab, Seif Eldawlatly, Gamal M Aly

Objective.Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technology.Approach.This study focuses on enhancing cross-subject (CS) MI EEG classification using EEG spectrum images. The proposed calibration-free approach employs deep learning techniques for MI classification and Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed WGAN generates synthetic spectrum images from the recorded MI-EEG to expand the training dataset; aiming to enhance the classifier's performance. The proposed approach eliminates the need for any calibration data from the target subject, making it more suitable for real-world applications.Main results.To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2 A, and IV-1 benchmark datasets, employing leave one-subject out validation. Our results demonstrate that using the proposed modified VGG-CNN classifier in addition to WGAN-generated data for augmentation leads to an enhancement in CS accuracy outperforming state-of-the-art methods.Significance.This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications.

目的:运动想象(MI)是脑机接口(BCI)的一个主要范例,其中用户依靠脑电图(EEG)信号来控制物体的运动。然而,由于受试者之间存在差异,MI BCI 需要记录受试者的相关数据,以训练机器学习分类器,用于识别预期的运动动作。这对 MI BCI 的开发是一个挑战,因为它使校准变得复杂,并阻碍了这种技术的广泛应用:本研究的重点是利用脑电图频谱图像加强跨主体 MI 脑电图分类。所提出的免校准方法采用深度学习技术进行 MI 分类,并采用 Wasserstein 生成对抗网络(WGAN)进行数据增强。拟议的 WGAN 可从记录的 MI-EEG 生成合成频谱图像,以扩展训练数据集,从而提高分类器的性能。所提出的方法无需目标对象的任何校准数据,因此更适合真实世界的应用:为了评估所提框架的稳健性和有效性,我们利用了BCI竞赛IV-2B、IV-2A和IV-1基准数据集,并进行了单对象排除验证。我们的研究结果表明,除了使用 WGAN 生成的数据进行增强外,使用所提出的改进型 VGG-CNN 分类器还能提高跨受试者准确性,其准确性优于最先进的方法:意义:这种方法代表着向开发免校准 BCI 系统迈进了一步,从而扩大了其应用范围。
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引用次数: 0
SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia. SQI-DOANet:基于脑电图的深度神经网络,用于估计信号质量指数和麻醉深度。
Pub Date : 2024-07-30 DOI: 10.1088/1741-2552/ad6592
Rui Yu, Zhuhuang Zhou, Meng Xu, Meng Gao, Meitong Zhu, Shuicai Wu, Xiaorong Gao, Guangyu Bin

Objective. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, during surgery electroencephalography (EEG) is usually subject to various disturbances that affect the accuracy of DOA. Therefore, accurately estimating noise in EEG and reliably assessing DOA remains an important challenge. In this paper, we proposed a signal quality index (SQI) network (SQINet) for assessing the EEG signal quality and a DOA network (DOANet) for analyzing EEG signals to precisely estimate DOA. The two networks are termed SQI-DOANet.Approach. The SQINet contained a shallow convolutional neural network to quickly determine the quality of the EEG signal. The DOANet comprised a feature extraction module for extracting features, a dual attention module for fusing multi-channel and multi-scale information, and a gated multilayer perceptron module for extracting temporal information. The performance of the SQI-DOANet model was validated by training and testing the model on the large VitalDB database, with the bispectral index (BIS) as the reference standard.Main results. The proposed DOANet yielded a Pearson correlation coefficient with the BIS score of 0.88 in the five-fold cross-validation, with a mean absolute error (MAE) of 4.81. The mean Pearson correlation coefficient of SQI-DOANet with the BIS score in the five-fold cross-validation was 0.82, with an MAE of 5.66.Significance. The SQI-DOANet model outperformed three compared methods. The proposed SQI-DOANet may be used as a new deep learning method for DOA estimation. The code of the SQI-DOANet will be made available publicly athttps://github.com/YuRui8879/SQI-DOANet.

目的: 在手术过程中监测麻醉深度(DOA)至关重要。然而,准确、实时地估计 DOA 仍然是一项具有挑战性的任务。在本文中,我们提出了一个用于评估脑电图(EEG)信号质量的信号质量指标(SQI)网络(SQINet)和一个用于分析 EEG 信号以精确估计 DOA 的 DOA 网络(DOANet)。这两个网络被称为 SQI-DOANet。 方法: SQINet 包含一个浅层卷积神经网络,用于快速确定脑电信号的质量。DOANet 包括用于提取特征的特征提取模块、用于融合多通道和多尺度信息的双注意模块,以及用于提取时间信息的门控多层感知器模块。以双谱指数(BIS)为参考标准,在大型 VitalDB 数据库上对 SQI-DOANet 模型进行了训练和测试,从而验证了该模型的性能。在 5 倍交叉验证中,SQI-DOANet 与 BIS 评分的平均皮尔逊相关系数为 0.82,平均绝对误差为 5.66。提出的 SQI-DOANet 可作为一种新的深度学习方法用于 DOA 估计。SQI-DOANet 的代码将在 https://github.com/YuRui8879/SQI-DOANet. 公开。
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引用次数: 0
Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials. Spiking Laguerre Volterra 网络--从局部场电位预测神经元活动。
Pub Date : 2024-07-29 DOI: 10.1088/1741-2552/ad6594
Kyriaki Kostoglou, Konstantinos P Michmizos, Pantelis Stathis, Damianos Sakas, Konstantina S Nikita, Georgios D Mitsis

Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

目的了解局部场电位(LFP)和神经元尖峰活动之间的生成机制是理解大脑信息处理的关键一步。迄今为止,大多数方法都是简单地量化局部场电位和尖峰活动之间的耦合。然而,很少有人能根据 LFP 的变化预测尖峰发生的确切时间:在此,我们提出了新型尖峰拉盖尔-伏特拉网络(sLVN)模型来描述 LFP 与尖峰之间的动态关系,从而填补了这一空白。与传统的人工神经网络相比,sLVN 是可解释的模型,能提供可解释的潜在动态特征:主要结果:所提出的网络被应用于帕金森病(PD)患者在接受脑深部刺激(DBS)手术期间的细胞外微电极记录。根据 LFP 穗对的可预测性,我们检测到三个具有独特信号特征和 sLVN 模型特征的神经元群:这些群组与 DBS 手术后运动评分的改善间接相关,因此有必要进一步研究尖峰活动可预测性作为术中生物标记的潜力,以优化 DBS 导联放置。
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引用次数: 0
Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters. 基于具有可调超参数的在线学习指标的动态条件下自适应肌电图分解。
Pub Date : 2024-07-29 DOI: 10.1088/1741-2552/ad5ebf
Irene Mendez Guerra, Deren Y Barsakcioglu, Dario Farina

Objective. Developing neural decoders robust to non-stationary conditions is essential to ensure their long-term accuracy and stability. This is particularly important when decoding the neural drive to muscles during dynamic contractions, which pose significant challenges for stationary decoders.Approach. We propose a novel adaptive electromyography (EMG) decomposition algorithm that builds on blind source separation methods by leveraging the Kullback-Leibler divergence and kurtosis of the signals as metrics for online learning. The proposed approach provides a theoretical framework to tune the adaptation hyperparameters and compensate for non-stationarities in the mixing matrix, such as due to dynamic contractions, and to identify the underlying motor neuron (MN) discharges. The adaptation is performed in real-time (∼22 ms of computational time per 100 ms batches).Main results. The hyperparameters of the proposed adaptation captured anatomical differences between recording locations (forearm vs wrist) and generalised across subjects. Once optimised, the proposed adaptation algorithm significantly improved all decomposition performance metrics with respect to the absence of adaptation in a wide range of motion of the wrist (80). The rate of agreement, sensitivity, and precision were⩾90%in⩾80%of the cases in both simulated and experimentally recorded data, according to a two-source validation approach.Significance. The findings demonstrate the suitability of the proposed online learning metrics and hyperparameter optimisation to compensate the induced modulation and accurately decode MN discharges in dynamic conditions. Moreover, the study proposes an experimental validation method for EMG decomposition in dynamic tasks.

目标:要开发稳定、准确的可穿戴神经接口,就必须具备对非稳态条件的鲁棒性:我们提出了一种新颖的自适应肌电图(EMG)分解算法,该算法以盲源分离方法为基础,利用信号的库尔贝-李卜勒发散和峰度作为在线学习的指标。所提出的方法提供了一个理论框架,用于调整适应超参数,补偿混合矩阵中的非稳态性(如动态收缩引起的非稳态性),并识别潜在的运动神经元(MN)放电。适应是实时进行的(每 100 毫秒批次的计算时间约为 22 毫秒):主要结果:在手腕大范围运动(80°)的情况下,与无适应性相比,所提出的适应性算法大大提高了所有分解性能指标。根据双源验证方法,在模拟数据和实验记录数据中,在≥80%的情况下,一致性、灵敏度和精确度均≥90%:研究结果表明,通过安装在手腕和前臂的可穿戴系统对动态收缩期间的 MN 放电进行实时精确解码是可行的。此外,该研究还为动态任务中的肌电图分解提出了一种实验验证方法。
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引用次数: 0
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Journal of neural engineering
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