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BIDSAlign: a library for automatic merging and preprocessing of multiple EEG repositories. BIDSAlign:用于自动合并和预处理多个脑电图库的库。
Pub Date : 2024-08-20 DOI: 10.1088/1741-2552/ad6a8c
Andrea Zanola, Federico Del Pup, Camillo Porcaro, Manfredo Atzori

Objective.This study aims to address the challenges associated with data-driven electroencephalography (EEG) data analysis by introducing a standardised library calledBIDSAlign. This library efficiently processes and merges heterogeneous EEG datasets from different sources into a common standard template. The goal of this work is to create an environment that allows to preprocess public datasets in order to provide data for the effective training of deep learning (DL) architectures.Approach.The library can handle both Brain Imaging Data Structure (BIDS) and non-BIDS datasets, allowing the user to easily preprocess multiple public datasets. It unifies the EEG recordings acquired with different settings by defining a common pipeline and a specified channel template. An array of visualisation functions is provided inside the library, together with a user-friendly graphical user interface to assist non-expert users throughout the workflow.Main results.BIDSAlign enables the effective use of public EEG datasets, providing valuable medical insights, even for non-experts in the field. Results from applying the library to datasets from OpenNeuro demonstrate its ability to extract significant medical knowledge through an end-to-end workflow, facilitating group analysis, visual comparison and statistical testing.Significance.BIDSAlign solves the lack of large EEG datasets by aligning multiple datasets to a standard template. This unlocks the potential of public EEG data for training DL models. It paves the way to promising contributions based on DL to clinical and non-clinical EEG research, offering insights that can inform neurological disease diagnosis and treatment strategies.

研究目的本研究旨在通过引入一个名为 BIDSAlign 的标准化库,解决与数据驱动脑电图(EEG)数据分析相关的挑战。该库可有效处理不同来源的异构脑电图数据集,并将其合并到一个通用的标准模板中。这项工作的目标是创建一个环境,允许对公共数据集进行预处理,以便为深度学习架构的有效训练提供数据。该库可以处理 BIDS(脑成像数据结构)和非 BIDS 数据集,让用户可以轻松预处理多个公共数据集。它通过定义通用管道和指定通道模板,统一了以不同设置获取的脑电图记录。库中提供了一系列可视化功能,以及用户友好的图形用户界面,可在整个工作流程中为非专业用户提供帮助。BIDSAlign 能够有效利用公共脑电图数据集,即使是该领域的非专业人员也能获得有价值的医学见解。将该库应用于 OpenNeuro 数据集的结果表明,它能够通过端到端工作流程提取重要的医学知识,促进分组分析、可视化比较和统计测试。BIDSAlign 通过将多个数据集与标准模板对齐,解决了缺乏大型脑电图数据集的问题。这释放了公共脑电图数据在训练深度学习模型方面的潜力。它为基于深度学习的临床和非临床脑电图研究铺平了道路,为神经疾病诊断和治疗策略提供了启示。
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引用次数: 0
EEG workload estimation and classification: a systematic review. 脑电图工作量估算与分类:系统综述。
Pub Date : 2024-08-16 DOI: 10.1088/1741-2552/ad705e
Jahid Hassan, Md Shamim Reza, Syed Udoy Ahmed, Nazmul Haque Anik, Md Obaydullah Khan

Objective: Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. ML and DL techniques have been increasingly employed to develop accurate workload estimation and classification models based on EEG data. The goal of this systematic review is to compile the body of research on EEG workload estimation and classification using ML and DL approaches.

Methods: The PRISMA procedures were followed in conducting the review, searches were conducted through databases at SpringerLink, ACM Digital Library, IEEE Explore, PUBMED, and Science Direct from the beginning to the end of February 16, 2024. Studies were selected based on predefined inclusion criteria. Data were extracted to capture study design, participant demographics, EEG features, ML/DL algorithms, and reported performance metrics.

Results: Out of the 125 items that emerged, 33 scientific papers were fully evaluated. The study designs, participant demographics, and EEG workload measurement and categorization techniques used in the investigations differed. SVM, CNN, and hybrid networks are examples of ML and DL approaches that were often used. Analyzing the accuracy scores achieved by different ML/DL models. Furthermore, a relationship was noted between sample frequency and model accuracy, with higher sample frequencies generally leading to improved performance. The percentage distribution of ML/DL methods revealed that SVMs, CNNs, and RNNs were the most commonly utilized techniques, reflecting their robustness in handling EEG data.

Significance: The comprehensive review emphasizes how ML may be used to identify mental workload across a variety of disciplines using EEG data. Optimizing practical applications requires multimodal data integration, standardization efforts, and real-world validation studies. These systems will also be further improved by addressing ethical issues and investigating new EEG properties, which will improve human-computer interaction and performance assessment.

目的:脑电图(EEG)已发展成为估算各领域认知工作量不可或缺的工具。人们越来越多地采用 ML 和 DL 技术来开发基于脑电图数据的精确工作量估算和分类模型。本系统性综述的目的是汇编使用 ML 和 DL 方法进行脑电图工作量估算和分类的研究成果:方法:在进行综述时遵循了 PRISMA 程序,并在 SpringerLink、ACM Digital Library、IEEE Explore、PUBMED 和 Science Direct 等数据库中进行了搜索,搜索时间从开始到 2024 年 2 月 16 日结束。根据预定义的纳入标准选择研究。提取的数据包括研究设计、参与者人口统计学特征、脑电图特征、ML/DL 算法以及报告的性能指标:在出现的 125 个项目中,有 33 篇科学论文得到了全面评估。研究设计、参与者人口统计学特征、脑电图工作量测量以及调查中使用的分类技术各不相同。SVM、CNN 和混合网络是经常使用的 ML 和 DL 方法。分析不同 ML/DL 模型达到的准确度分数。此外,我们还注意到样本频率与模型准确度之间的关系,样本频率越高,性能通常越好。ML/DL 方法的百分比分布显示,SVMs、CNNs 和 RNNs 是最常用的技术,反映了它们在处理脑电图数据时的鲁棒性:这篇综合评论强调了如何利用脑电图数据将 ML 用于识别各学科的心理工作量。优化实际应用需要多模态数据整合、标准化工作和真实世界验证研究。这些系统还将通过解决伦理问题和研究新的脑电图特性得到进一步改进,从而改善人机交互和绩效评估。
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引用次数: 0
Affinity-based drug delivery systems for the central nervous system: exploiting molecular interactions for local, precise targeting. 用于中枢神经系统的基于亲和力的给药系统:利用分子相互作用实现局部精确靶向。
Pub Date : 2024-08-14 DOI: 10.1088/1741-2552/ad680a
Pablo Ramos Ferrer, Shelly Sakiyama-Elbert

Objective: The effective treatment of central nervous system (CNS) disorders remains a significant challenge, primarily due to its molecular and structural complexity. Clinical translation of promising therapeutic agents is limited by the lack of optimal drug delivery systems capable of targeted, localized release of drugs to the brain and spinal cord.Approach: This review provides an overview of the potential of affinity-based drug delivery systems, which leverage molecular interactions to enhance the delivery and efficacy of therapeutic agents within the CNS.Main results: Various approaches, including hydrogels, micro- and nanoparticles, and functionalized biomaterials, are examined for their ability to provide local, sustained release of proteins, growth factors and other drugs.Significance: Furthermore, we present a detailed analysis of design considerations for developing effective affinity-based systems, incorporating insights from both existing literature and our group's research. These considerations include the biochemical modification of delivery vehicles and the optimization of physical and chemical properties to improve therapeutic outcomes.

中枢神经系统(CNS)疾病的有效治疗仍然是一项重大挑战,这主要是由于其分子和结构的复杂性。由于缺乏能够向大脑和脊髓定向、局部释放药物的最佳给药系统,有前景的治疗药物的临床转化受到了限制。本综述概述了基于亲和力的给药系统的潜力,这些系统利用分子间的相互作用来提高治疗药物在中枢神经系统内的给药和疗效。我们研究了包括水凝胶、微型和纳米颗粒以及功能化生物材料在内的各种方法,以了解它们在局部持续释放蛋白质、生长因子和其他药物方面的能力。此外,我们还结合现有文献和本研究小组的研究成果,详细分析了开发有效亲和系统的设计注意事项。这些考虑因素包括对递送载体进行生化修饰以及优化物理和化学特性,以改善治疗效果。
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引用次数: 0
Coupled Eulerian-Lagrangian model prediction of neural tissue strain during microelectrode insertion. 欧拉-拉格朗日耦合模型预测微电极插入过程中的神经组织应变。
Pub Date : 2024-08-14 DOI: 10.1088/1741-2552/ad68a6
K P O'Sullivan, B Coats

Objective.Implanted neural microelectrodes are an important tool for recording from and stimulating the cerebral cortex. The performance of chronically implanted devices, however, is often hindered by the development of a reactive tissue response. Previous computational models have investigated brain strain from micromotions of neural electrodes after they have been inserted, to investigate design parameters that might minimize triggers to the reactive tissue response. However, these models ignore tissue damage created during device insertion, an important contributing factor to the severity of inflammation. The objective of this study was to evaluate the effect of electrode geometry, insertion speed, and surface friction on brain tissue strain during insertion.Approach. Using a coupled Eulerian-Lagrangian approach, we developed a 3D finite element model (FEM) that simulates the dynamic insertion of a neural microelectrode in brain tissue. Geometry was varied to investigate tip bluntness, cross-sectional shape, and shank thickness. Insertion velocities were varied from 1 to 8 m s-1. Friction was varied from frictionless to 0.4. Tissue strain and potential microvasculature hemorrhage radius were evaluated for brain regions along the electrode shank and near its tip.Main results. Sharper tips resulted in higher mean max principal strains near the tip except for the bluntest tip on the square cross-section electrode, which exhibited high compressive strain values due to stress concentrations at the corners. The potential vascular damage radius around the electrode was primarily a function of the shank diameter, with smaller shank diameters resulting in smaller distributions of radial strain around the electrode. However, the square shank interaction with the tip taper length caused unique strain distributions that increased the damage radius in some cases. Faster insertion velocities created more strain near the tip but less strain along the shank. Increased friction between the brain and electrode created more strain near the electrode tip and along the shank, but frictionless interactions resulted in increased tearing of brain tissue near the tip.Significance. These results demonstrate the first dynamic FEM study of neural electrode insertion, identifying design factors that can reduce tissue strain and potentially mitigate initial reactive tissue responses due to traumatic microelectrode array insertion.

目的: 植入式神经微电极是记录和刺激大脑皮层的重要工具。然而,长期植入装置的性能往往受到反应性组织反应的影响。以前的计算模型研究了神经电极插入后微动产生的脑应变,以研究可最大限度减少触发反应性组织反应的设计参数。然而,这些模型忽略了设备插入过程中造成的组织损伤,而这是导致炎症严重程度的一个重要因素。本研究旨在评估电极几何形状、插入速度和表面摩擦对插入过程中脑组织应变的影响:方法:我们使用欧拉-拉格朗日(CEL)耦合方法开发了一个三维有限元模型(FEM),模拟神经微电极在脑组织中的动态插入。通过改变几何形状来研究针尖钝度、横截面形状和针柄厚度。插入速度从 1 米/秒到 8 米/秒不等。摩擦力从无摩擦到 0.4 不等。除了方形截面电极上最钝的尖端外,其他尖端附近的组织应变和潜在微血管出血半径均较高。电极周围潜在的血管损伤半径主要是柄部直径的函数,柄部直径越小,电极周围的径向应变分布越小。然而,方形柄与尖端锥度长度的相互作用造成了独特的应变分布,在某些情况下增加了损伤半径。更快的插入速度会在尖端附近产生更多应变,但沿柄的应变较小。大脑和电极之间的摩擦增加会在电极尖端附近和沿柄产生更多应变,但无摩擦的相互作用会导致尖端附近的脑组织撕裂增加:这些结果首次展示了神经电极插入的动态有限元研究,确定了可以减少组织应变的设计因素,并有可能减轻创伤性微电极阵列插入引起的初始反应性组织反应。
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引用次数: 0
Delayed administration of interleukin-4 coacervate alleviates the neurotoxic phenotype of astrocytes and promotes functional recovery after a contusion spinal cord injury. 延迟给药白细胞介素-4胶囊可减轻星形胶质细胞的神经毒性表型,并促进挫伤性脊髓损伤后的功能恢复。
Pub Date : 2024-08-14 DOI: 10.1088/1741-2552/ad6596
Manoj K Gottipati, Anthony R D'Amato, Jayant Saksena, Phillip G Popovich, Yadong Wang, Ryan J Gilbert

Objective. Macrophages and astrocytes play a crucial role in the aftermath of a traumatic spinal cord injury (SCI). Infiltrating macrophages adopt a pro-inflammatory phenotype while resident astrocytes adopt a neurotoxic phenotype at the injury site, both of which contribute to neuronal death and inhibit axonal regeneration. The cytokine interleukin-4 (IL-4) has shown significant promise in preclinical models of SCI by alleviating the macrophage-mediated inflammation and promoting functional recovery. However, its effect on neurotoxic reactive astrocytes remains to be elucidated, which we explored in this study. We also studied the beneficial effects of a sustained release of IL-4 from an injectable biomaterial compared to bolus administration of IL-4.Approach. We fabricated a heparin-based coacervate capable of anchoring and releasing bioactive IL-4 and tested its efficacyin vitroandin vivo. Main results. We show that IL-4 coacervate is biocompatible and drives a robust anti-inflammatory macrophage phenotype in culture. We also show that IL-4 and IL-4 coacervate can alleviate the reactive neurotoxic phenotype of astrocytes in culture. Finally, using a murine model of contusion SCI, we show that IL-4 and IL-4 coacervate, injected intraspinally 2 d post-injury, can reduce macrophage-mediated inflammation, and alleviate neurotoxic astrocyte phenotype, acutely and chronically, while also promoting neuroprotection with significant improvements in hindlimb locomotor recovery. We observed that IL-4 coacervate can promote a more robust regenerative macrophage phenotypein vitro, as well as match its efficacyin vivo,compared to bolus IL-4.Significance. Our work shows the promise of coacervate as a great choice for local and prolonged delivery of cytokines like IL-4. We support this by showing that the coacervate can release bioactive IL-4, which acts on macrophages and astrocytes to promote a pro-regenerative environment following a SCI leading to robust neuroprotective and functional outcomes.

目的:巨噬细胞和星形胶质细胞在创伤性脊髓损伤(SCI)后起着至关重要的作用。浸润的巨噬细胞具有促炎表型,而驻留在损伤部位的星形胶质细胞则具有神经毒性表型,两者都会导致神经元死亡并抑制轴突再生。细胞因子白细胞介素-4(IL-4)通过减轻巨噬细胞介导的炎症和促进功能恢复,在 SCI 临床前模型中显示出了巨大的前景。然而,它对神经毒性反应性星形胶质细胞的影响仍有待阐明,我们在本研究中对此进行了探讨。我们还研究了从可注射生物材料中持续释放IL-4的益处:方法:我们制作了一种能够固定和释放生物活性 IL-4 的肝素基凝聚剂,并在体外和体内测试了其疗效:主要结果:我们发现IL-4包被物具有生物相容性,并能在培养过程中产生强大的抗炎巨噬细胞表型。我们还发现,IL-4 和 IL-4 包被物可以减轻星形胶质细胞在培养过程中的反应性神经毒性表型。最后,我们利用小鼠挫伤性 SCI 模型表明,在小鼠受伤后 2 天进行椎管内注射 IL-4 和 IL-4 包被液,可以减少巨噬细胞介导的炎症反应,缓解急性和慢性神经毒性星形胶质细胞表型,同时还能促进神经保护,显著改善后肢运动功能的恢复。我们观察到,与栓剂IL-4相比,IL-4凝聚剂能在体外促进更强大的再生巨噬细胞表型,并在体内发挥与之相匹配的功效:我们的工作表明,共蒸物是局部和长时间输送 IL-4 等细胞因子的理想选择。我们的研究结果表明,包衣能释放生物活性 IL-4,它能作用于巨噬细胞和星形胶质细胞,促进脊髓损伤后的再生环境,从而产生强大的神经保护和功能性结果。
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引用次数: 0
In vivooptogenetics using a Utah Optrode Array with enhanced light output and spatial selectivity. 使用具有增强光输出和空间选择性的犹他光电极阵列进行体内光遗传学研究。
Pub Date : 2024-08-14 DOI: 10.1088/1741-2552/ad69c3
Niall McAlinden, Christopher F Reiche, Andrew M Clark, Robert Scharf, Yunzhou Cheng, Rohit Sharma, Loren Rieth, Martin D Dawson, Alessandra Angelucci, Keith Mathieson, Steve Blair

Objective.Optogenetics allows the manipulation of neural circuitsin vivowith high spatial and temporal precision. However, combining this precision with control over a significant portion of the brain is technologically challenging (especially in larger animal models).Approach.Here, we have developed, optimised, and testedin vivo, the Utah Optrode Array (UOA), an electrically addressable array of optical needles and interstitial sites illuminated by 181μLEDs and used to optogenetically stimulate the brain. The device is specifically designed for non-human primate studies.Main results.Thinning the combinedμLED and needle backplane of the device from 300μm to 230μm improved the efficiency of light delivery to tissue by 80%, allowing lowerμLED drive currents, which improved power management and thermal performance. The spatial selectivity of each site was also improved by integrating an optical interposer to reduce stray light emission. These improvements were achieved using an innovative fabrication method to create an anodically bonded glass/silicon substrate with through-silicon vias etched, forming an optical interposer. Optical modelling was used to demonstrate that the tip structure of the device had a major influence on the illumination pattern. The thermal performance was evaluated through a combination of modelling and experiment, in order to ensure that cortical tissue temperatures did not rise by more than 1 °C. The device was testedin vivoin the visual cortex of macaque expressing ChR2-tdTomato in cortical neurons.Significance.It was shown that the UOA produced the strongest optogenetic response in the region surrounding the needle tips, and that the extent of the optogenetic response matched the predicted illumination profile based on optical modelling-demonstrating the improved spatial selectivity resulting from the optical interposer approach. Furthermore, different needle illumination sites generated different patterns of low-frequency potential activity.

目的:光遗传学可以在体内对神经回路进行高空间和时间精度的操作。然而,要将这种精确性与对大脑大部分区域的控制相结合,在技术上具有挑战性(尤其是在大型动物模型中):在此,我们开发、优化并在体内测试了犹他州光针阵列(UOA),这是一个由 181 µLED 照亮的光针和间隙点组成的可寻址电阵列,用于对大脑进行光遗传刺激。该设备专为非人灵长类研究而设计:主要结果:将该装置的µLED和针背板的厚度从300微米减薄至230微米,将光传递到组织的效率提高了80%,从而降低了µLED驱动电流,改善了电源管理和散热性能。此外,通过集成光学中间件以减少杂散光发射,还提高了每个点的空间选择性。这些改进都是通过创新的制造方法实现的,该方法采用阳极键合玻璃/硅基板,蚀刻硅通孔,形成光学中间件。光学建模证明,器件的尖端结构对照明模式有重大影响。通过建模和实验相结合的方法对热性能进行了评估,以确保皮质组织温度上升不超过 1°C。该装置在表达 ChR2-tdTomato 的猕猴视觉皮层神经元中进行了活体测试:研究表明,UOA 在针尖周围区域产生了最强的光遗传反应,而且光遗传反应的范围与根据光学建模预测的照明轮廓相吻合--这表明光学内插方法提高了空间选择性。此外,不同的针头照明位置会产生不同的低频电位(LFP)活动模式。
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引用次数: 0
Choosing the right electrode representation for modeling real bioelectronic interfaces: a comprehensive guide. 为真实生物电子界面建模选择正确的电极表示法:综合指南。
Pub Date : 2024-08-14 DOI: 10.1088/1741-2552/ad6a8b
Aleksandar Opančar, Eric Daniel Głowacki, Vedran Đerek

Objective.Producing realistic numerical models of neurostimulation electrodes in contact with the electrolyte and tissue, for use in time-domain finite element method simulations while maintaining a reasonable computational burden remains a challenge. We aim to provide a straightforward experimental-theoretical hybrid approach for common electrode materials (Ti, TiN, ITO, Au, Pt, IrOx) that are relevant to the research field of bioelectronics, along with all the information necessary to replicate our approach in arbitrary geometry for real-life experimental applications.Approach.We used electrochemical impedance spectroscopy (EIS) to extract the electrode parameters in the AC regime under different DC biases. The pulsed electrode response was obtained by fast amperometry (FA) to optimize and verify the previously obtained electrode parameters in a COMSOL Multiphysics model. For optimization of the electrode parameters a constant phase element (CPE) needed to be implemented in time-domain.Main results.We find that the parameters obtained by EIS can be used to accurately simulate pulsed response only close to the electrode open circuit potential, while at other potentials we give corrections to the obtained parameters, based on FA measurements. We also find that for many electrodes (Au, TiN, Pt, and IrOx), it is important to implement a distributed CPE rather than an ideal capacitor for estimating the electrode double-layer capacitance. We outline and provide examples for the novel time-domain implementation of the CPE for finite element method simulations in COMSOL Multiphysics.Significance.An overview of electrode parameters for some common electrode materials can be a valuable and useful tool in numerical bioelectronics models. A provided FEM implementation model can be readily adapted to arbitrary electrode geometries and used for various applications. Finally, the presented methodology for parametrization of electrode materials can be used for any materials of interest which were not covered by this work.

目标: 制作神经刺激电极与电解质和组织接触的逼真数值模型,用于时域有限元法模拟,同时保持合理的计算负担,仍然是一项挑战。我们的目标是为与生物电子学研究领域相关的常见电极材料(Ti、TiN、ITO、Au、Pt、IrOx)提供一种简单明了的实验-理论混合方法,以及将我们的方法复制到现实生活实验应用中的任意几何形状所需的所有信息。通过快速安培计获得脉冲电极响应,以便在 COMSOL 多物理场模型中优化和验证之前获得的电极参数。为了优化电极参数,需要在时域中实施恒定相位元素。主要结果: 我们发现,通过电化学阻抗谱获得的参数仅可用于精确模拟接近电极开路电位的脉冲响应,而在其他电位下,我们会根据快速安培计测量结果对获得的参数进行修正。我们还发现,对于许多电极(Au、TiN、Pt 和 IrOx)来说,重要的是采用分布式恒定相位元件而不是理想电容器来估算电极双层电容。我们概述了恒定相位元素在 COMSOL Multiphysics 中用于有限元法模拟的新型时域实施方法,并提供了相关示例。所提供的有限元实现模型可随时适应任意的电极几何形状,并用于不同的应用。最后,所介绍的电极材料参数化方法可用于本研究未涉及的任何相关材料。
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引用次数: 0
Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification. 基于梯度惩罚的 Wasserstein 生成对抗网络和卷积神经网络的运动图像脑电图分类。
Pub Date : 2024-08-14 DOI: 10.1088/1741-2552/ad6cf5
Hui Xiong, Jiahe Li, Jinzhen Liu, Jinlong Song, Yuqing Han

Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.

目的:由于难以获得运动图像脑电图(MI-EEG)数据并确保其质量,训练数据不足往往导致基于深度学习的分类网络过度拟合和泛化能力不足。因此,我们提出了一种新颖的数据增强方法和深度学习分类模型,以进一步提高 MI-EEG 的解码性能。通过连续小波变换,将原始脑电信号转换成时频图,作为模型的输入。提出了一种改进的 Wasserstein 生成对抗网络与梯度惩罚数据增强方法,有效地扩展了用于模型训练的数据集。此外,还设计了一个简洁高效的深度学习模型,以进一步提高解码性能。通过多种数据评估方法的验证,证明了所提出的生成网络可以生成更真实的数据。在 BCI Competition IV 2a 和 2b 数据集以及实际收集的数据集上的实验结果表明,分类准确率分别为 83.4%、89.1% 和 73.3%,Kappa 值分别为 0.779、0.782 和 0.644。结果表明,所提出的模型优于最先进的方法。实验结果表明,该方法有效地增强了 MI-EEG 数据,减轻了分类网络的过拟合,提高了 MI 分类的准确性,对 MI 任务具有积极意义。
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引用次数: 0
SAFE-OPT: a Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints. SAFE-OPT:贝叶斯优化算法,用于学习具有安全约束的最佳深部脑刺激参数。
Pub Date : 2024-08-14 DOI: 10.1088/1741-2552/ad6cf3
Eric R Cole, Mark J Connolly, Mihir Ghetiya, Mohammad E S Sendi, Adam Kashlan, Thomas E Eggers, Robert E Gross

Objective.To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.Approach.In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searchingin silico. Main results.We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjectsin vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject's safety threshold.Significance.The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.

目的: 利用深部脑刺激治疗神经和精神疾病时,训练有素的临床医生必须通过监测每位患者的症状和副作用,在长达数月的试错过程中为其选择参数,从而延误了最佳临床治疗效果。有人提出贝叶斯优化法是一种快速自动搜索最佳参数的有效方法。然而,传统的贝叶斯优化并不考虑患者的安全,可能会引发不必要或危险的副作用:在这项研究中,我们开发了 SAFE-OPT,这是一种贝叶斯优化算法,旨在学习特定受试者的安全约束,以避免在优化过程中出现潜在的有害刺激设置。我们利用啮齿类动物多电极刺激范例对 SAFE-OPT 进行了原型设计和验证,该范例会导致受试者在空间记忆任务中出现特定的表现缺陷。我们首先利用一组初始受试者的数据建立了一个模拟,并在此基础上设计了最佳的 SAFE-OPT 配置,以实现安全、准确的硅搜索。 主要结果: 然后,我们在体内的新受试者身上部署了 SAFE-OPT 和传统的贝叶斯优化,结果表明 SAFE-OPT 可以找到不损害任务性能的最佳高刺激振幅,其样本效率与贝叶斯优化相当,而且不会选择超出受试者安全阈值的振幅值。 结论: 安全约束的加入将为在脑深部刺激的实际应用中采用贝叶斯优化法迈出关键一步。
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引用次数: 0
Endovascular stimulation of the pudendal nerve using a stent-mounted electrode array. 使用安装在支架上的电极阵列对阴部神经进行血管内刺激。
Pub Date : 2024-08-13 DOI: 10.1088/1741-2552/ad6590
JingYang Liu, David B Grayden, Janet R Keast, Sam E John

Objective. Previous preclinical and clinical studies have demonstrated that pudendal nerve is a promising target for restoring bladder control. The spatial proximity between the pudendal nerve and its accompanying blood vessels in the pudendal canal provides an opportunity for endovascular neurostimulation, which is a less invasive approach compared to conventional chronically implanted electrodes. In this study, we investigated the feasibility of excitatory stimulation and kilohertz-frequency block of the compound pudendal nerve in sheep using a stent-mounted electrode array.Approach. In a set of acute animal experiments, a commercially available hexapolar electrode catheter was introduced in the unilateral internal pudendal artery to deliver bipolar electrical stimulation of the adjacent compound pudendal nerve. The catheter electrode was replaced with a custom-made stent-mounted electrode array and the stimulation sessions were repeated. Global electromyogram activity of the pelvic floor and related sphincter muscles was recorded with a monopolar electrode placed within the urethra concurrently.Main results. We demonstrated the feasibility of endovascular stimulation of the pudendal nerve with both electrode types. The threshold current of endovascular stimulation was influenced by electrode-nerve distance and electrode orientation. Increasing the axial inter-electrode distance significantly decreased threshold current. Endovascular kilohertz-frequency nerve block was possible with the electrode catheter.Significance. The present study demonstrated that endovascular stimulation of the pudendal nerve with the stent-mounted electrode array may be a promising less invasive alternative to conventional implantable electrodes, which has important clinical implications in the treatment of urinary incontinence. Endovascular blocking of pudendal nerve may provide an alternative solution to the bladder-sphincter dyssynergia problem in bladder management for people with spinal cord injury.

目的:以往的临床前和临床研究表明,阴部神经是恢复膀胱控制的一个很有前景的靶点。阴部神经和其在阴部管内的伴行血管之间的空间接近性为血管内神经刺激提供了机会,与传统的慢性植入电极相比,这是一种创伤较小的方法。在这项研究中,我们利用安装在支架上的电极阵列研究了对绵羊的复发性阴部神经进行兴奋刺激和千赫兹频率阻断的可行性:在一组急性动物实验中,将市售的六极电极导管插入单侧阴茎内动脉,对邻近的复发性阴茎神经进行双极电刺激。将导管电极更换为定制的支架安装电极阵列,然后重复刺激疗程。同时记录了尿道外括约肌的整体肌电图(EMG)活动:我们证明了用两种电极类型对阴茎神经传出进行血管内刺激的可行性。血管内刺激的阈值电流受电极-神经间距和电极方向的影响。增加电极间的轴向距离可显著降低阈值电流。使用电极导管可进行血管内千赫频率神经阻滞:本研究表明,用安装在支架上的电极阵列对阴茎神经进行血管内刺激可能是一种很有前途的微创疗法,可替代传统的植入式电极,这对治疗尿失禁具有重要的临床意义。在脊髓损伤患者的膀胱管理中,血管内阻断阴部神经传出可能是解决膀胱括约肌协同障碍问题的另一种方法。
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引用次数: 0
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Journal of neural engineering
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