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Relating EEG to continuous speech using deep neural networks: a review. 利用深度神经网络将脑电图与连续语音联系起来:综述。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-08-03 DOI: 10.1088/1741-2552/ace73f
Corentin Puffay, Bernd Accou, Lies Bollens, Mohammad Jalilpour Monesi, Jonas Vanthornhout, Hugo Van Hamme, Tom Francart

Objective.When a person listens to continuous speech, a corresponding response is elicited in the brain and can be recorded using electroencephalography (EEG). Linear models are presently used to relate the EEG recording to the corresponding speech signal. The ability of linear models to find a mapping between these two signals is used as a measure of neural tracking of speech. Such models are limited as they assume linearity in the EEG-speech relationship, which omits the nonlinear dynamics of the brain. As an alternative, deep learning models have recently been used to relate EEG to continuous speech.Approach.This paper reviews and comments on deep-learning-based studies that relate EEG to continuous speech in single- or multiple-speakers paradigms. We point out recurrent methodological pitfalls and the need for a standard benchmark of model analysis.Main results.We gathered 29 studies. The main methodological issues we found are biased cross-validations, data leakage leading to over-fitted models, or disproportionate data size compared to the model's complexity. In addition, we address requirements for a standard benchmark model analysis, such as public datasets, common evaluation metrics, and good practices for the match-mismatch task.Significance.We present a review paper summarizing the main deep-learning-based studies that relate EEG to speech while addressing methodological pitfalls and important considerations for this newly expanding field. Our study is particularly relevant given the growing application of deep learning in EEG-speech decoding.

目标。当一个人听到连续的讲话时,大脑会产生相应的反应,并可以用脑电图(EEG)记录下来。目前使用线性模型将脑电图记录与相应的语音信号联系起来。线性模型找到这两个信号之间的映射的能力被用作语音神经跟踪的测量。这样的模型是有限的,因为它们假设脑电图-言语关系是线性的,而忽略了大脑的非线性动力学。作为一种替代方法,深度学习模型最近被用于将脑电图与连续语音联系起来。本文回顾和评论了基于深度学习的研究,这些研究将脑电图与连续语音在单说话者或多说话者范式中联系起来。我们指出了反复出现的方法缺陷和对模型分析标准基准的需要。主要的结果。我们收集了29项研究。我们发现的主要方法问题是有偏差的交叉验证,导致模型过度拟合的数据泄漏,或者与模型复杂性相比不成比例的数据大小。此外,我们还讨论了标准基准模型分析的要求,如公共数据集、通用评估指标和匹配-不匹配任务的良好实践。意义。我们提出了一篇综述论文,总结了将脑电图与语音联系起来的主要基于深度学习的研究,同时解决了这个新扩展领域的方法缺陷和重要注意事项。鉴于深度学习在脑电图语音解码中的应用日益增多,我们的研究尤其相关。
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引用次数: 10
Structure-function dynamics of engineered, modular neuronal networks with controllable afferent-efferent connectivity. 具有可控传入-传出连接的工程模块化神经网络的结构-功能动力学。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-08-03 DOI: 10.1088/1741-2552/ace37f
Nicolai Winter-Hjelm, Åste Brune Tomren, Pawel Sikorski, Axel Sandvig, Ioanna Sandvig

Objective.Microfluidic devices interfaced with microelectrode arrays have in recent years emerged as powerful platforms for studying and manipulatingin vitroneuronal networks at the micro- and mesoscale. By segregating neuronal populations using microchannels only permissible to axons, neuronal networks can be designed to mimic the highly organized, modular topology of neuronal assemblies in the brain. However, little is known about how the underlying topological features of such engineered neuronal networks contribute to their functional profile. To start addressing this question, a key parameter is control of afferent or efferent connectivity within the network.Approach.In this study, we show that a microfluidic device featuring axon guiding channels with geometrical constraints inspired by a Tesla valve effectively promotes unidirectional axonal outgrowth between neuronal nodes, thereby enabling us to control afferent connectivity.Main results.Our results moreover indicate that these networks exhibit a more efficient network organization with higher modularity compared to single nodal controls. We verified this by applying designer viral tools to fluorescently label the neurons to visualize the structure of the networks, combined with extracellular electrophysiological recordings using embedded nanoporous microelectrodes to study the functional dynamics of these networks during maturation. We furthermore show that electrical stimulations of the networks induce signals selectively transmitted in a feedforward fashion between the neuronal populations.Significance.A key advantage with our microdevice is the ability to longitudinally study and manipulate both the structure and function of neuronal networks with high accuracy. This model system has the potential to provide novel insights into the development, topological organization, and neuroplasticity mechanisms of neuronal assemblies at the micro- and mesoscale in healthy and perturbed conditions.

目标。近年来,与微电极阵列相结合的微流控装置已成为研究和操纵微中尺度外神经元网络的有力平台。通过使用仅允许轴突的微通道分离神经元群,神经元网络可以被设计成模仿大脑中高度组织化、模块化的神经元组合拓扑结构。然而,人们对这种工程神经网络的潜在拓扑特征如何影响其功能概况知之甚少。为了解决这个问题,一个关键参数是控制网络内的传入或传出连通性。方法在本研究中,我们展示了一种微流体装置,其特征是轴突引导通道受特斯拉阀的几何约束,有效地促进了神经元节点之间的单向轴突生长,从而使我们能够控制传入连通性。主要的结果。我们的研究结果还表明,与单节点控制相比,这些网络表现出更有效的网络组织,具有更高的模块化。我们通过应用设计病毒工具对神经元进行荧光标记以可视化网络结构来验证这一点,并结合使用嵌入式纳米孔微电极的细胞外电生理记录来研究这些网络在成熟过程中的功能动力学。我们进一步表明,神经网络的电刺激诱导信号在神经元群之间以前馈方式选择性地传递。意义我们的微设备的一个关键优势是能够以高精度纵向研究和操纵神经网络的结构和功能。该模型系统有可能为健康和受干扰条件下的微观和中尺度神经元组装的发育、拓扑组织和神经可塑性机制提供新的见解。
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引用次数: 2
Biohybrid restoration of the hippocampal loop re-establishes the non-seizing state in anin vitromodel of limbic seizures. 海马体环生物杂交修复在脑边缘癫痫的体外模型中重建非癫痫状态。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-08-03 DOI: 10.1088/1741-2552/ace931
Davide Caron, Stefano Buccelli, Angel Canal-Alonso, Javad A Farsani, Giacomo Pruzzo, Bernabe Linares-Barranco, Juan Manuel Corchado, Michela Chiappalone, Gabriella Panuccio

Objective. The compromise of the hippocampal loop is a hallmark of mesial temporal lobe epilepsy (MTLE), the most frequent epileptic syndrome in the adult population and the most often refractory to medical therapy. Hippocampal sclerosis is found in >50% of drug-refractory MTLE patients and primarily involves the CA1, consequently disrupting the hippocampal output to the entorhinal cortex (EC). Closed-loop deep brain stimulation is the latest frontier to improve drug-refractory MTLE; however, current approaches do not restore the functional connectivity of the hippocampal loop, they are designed by trial-and-error and heavily rely on seizure detection or prediction algorithms. The objective of this study is to evaluate the anti-ictogenic efficacy and robustness of an artificial bridge restoring the dialog between hippocampus and EC.Approach. In mouse hippocampus-EC slices treated with 4-aminopyridine and in which the Schaffer Collaterals are severed, we established an artificial bridge between hippocampus and EC wherein interictal discharges originating in the CA3 triggered stimulation of the subiculum so to entrain EC networks. Combining quantification of ictal activity with tools from information theory, we addressed the efficacy of the bridge in controlling ictogenesis and in restoring the functional connectivity of the hippocampal loop.Main results. The bridge significantly decreased or even prevented ictal activity and proved robust to failure; when operating at 100% of its efficiency (i.e., delivering a pulse upon each interictal event), it recovered the functional connectivity of the hippocampal loop to a degree similar to what measured in the intact circuitry. The efficacy and robustness of the bridge stem in mirroring the adaptive properties of the CA3, which acts as biological neuromodulator.Significance. This work is the first stepping stone toward a paradigm shift in the conceptual design of stimulation devices for epilepsy treatment, from function control to functional restoration of the salient brain circuits.

目标。海马环的损害是内侧颞叶癫痫(MTLE)的一个标志,这是成人人群中最常见的癫痫综合征,也是最难以药物治疗的。海马硬化在50%以上的耐药MTLE患者中发现,主要涉及CA1,因此破坏海马向内嗅皮层(EC)的输出。闭环脑深部刺激是改善难治性MTLE的最新前沿;然而,目前的方法并不能恢复海马体环路的功能连接,它们是通过反复试验设计的,并且严重依赖于癫痫检测或预测算法。本研究的目的是评估人工桥恢复海马和ec之间对话的抗ictogenic功效和稳健性。在4-氨基吡啶处理的小鼠海马-EC切片中,我们在海马和EC之间建立了一个人工桥,其中源自CA3的间期放电触发了对下托的刺激,从而使EC网络进入。结合信息论的工具和量化的脑电图活动,我们探讨了脑桥在控制脑电图发生和恢复海马环路功能连接方面的功效。主要的结果。桥梁显著减少甚至阻止了致命活动,并证明了其抗破坏能力;当它以100%的效率运行时(即在每个间隔事件上传递脉冲),它恢复了海马体环路的功能连通性,其程度与在完整电路中测量的程度相似。桥干的有效性和鲁棒性反映了作为生物神经调节剂的CA3的自适应特性。这项工作是癫痫治疗刺激装置概念设计范式转变的第一步,从功能控制到显著脑回路的功能恢复。
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引用次数: 0
High-density magnetomyography is superior to high-density surface electromyography for motor unit decomposition: a simulation study. 在运动单元分解的模拟研究中,高密度磁肌图优于高密度表面肌电图。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-08-03 DOI: 10.1088/1741-2552/ace7f7
Thomas Klotz, Lena Lehmann, Francesco Negro, Oliver Röhrle

Objective.Studying motor units is essential for understanding motor control, the detection of neuromuscular disorders and the control of human-machine interfaces. Individual motor unit firings are currently identifiedin vivoby decomposing electromyographic (EMG) signals. Due to our body's properties and anatomy, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields do not interact with human tissues. This physical property and the emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored.Approach.In this work, we performin silicotrials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy.Main results.It is shown that non-invasive high-density MMG data is superior over comparable high-density surface EMG data for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 76%. Notably, MMG exhibits a less pronounced bias to detect superficial motor units.Significance.The presented simulations provide insights into methods to study the neuromuscular system non-invasively andin vivothat would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.

目标。研究运动单元对于理解运动控制、神经肌肉疾病的检测和人机界面的控制至关重要。目前,个体运动单元的放电是通过分解肌电图(EMG)信号在体内识别的。由于我们身体的特性和解剖结构,单个运动单元只能在有限的程度上通过表面肌电图分离。与电信号不同,磁场不与人体组织相互作用。这种物理性质和量子传感器的新兴技术使磁层析成像(MMG)成为一种非常有前途的方法。然而,MMG在研究神经肌肉生理学方面的全部潜力尚未被探索。在这项工作中,我们进行了硅试验,将肌电图和MMG的生物物理模型与最先进的运动单元分解算法相结合。这允许对运动单元分解精度的上界进行预测。主要的结果。研究表明,非侵入性高密度MMG数据在识别单个运动单元的放电模式方面优于可比的高密度表面肌电数据。分解MMG而不是肌电图使可识别的运动单元数量增加了76%。值得注意的是,MMG在检测浅表运动单位方面表现出不太明显的偏差。意义:所提出的模拟为非侵入性和活体研究神经肌肉系统的方法提供了见解,这是其他方法难以实现的。因此,本研究对生物医学新技术的发展具有指导意义。
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引用次数: 0
The role of vowel and consonant onsets in neural tracking of natural speech 元音和辅音起始点在自然语音神经跟踪中的作用
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-07-31 DOI: 10.1088/1741-2552/ad1784
Mohammad Jalilpour Monesi, Jonas Vanthornhout, T. Francart, Hugo Van hamme
Objective. To investigate how the auditory system processes natural speech, models have been created to relate the electroencephalography (EEG) signal of a person listening to speech to various representations of the speech. Mainly the speech envelope has been used, but also phonetic representations. We investigated to which degree of granularity phonetic representations can be related to the EEG signal. Approach. We used recorded EEG signals from 105 subjects while they listened to fairy tale stories. We utilized speech representations, including onset of any phone, vowel-consonant onsets, broad phonetic class (BPC) onsets, and narrow phonetic class (NPC) onsets, and related them to EEG using forward modeling and match-mismatch tasks. In forward modeling, we used a linear model to predict EEG from speech representations. In the match-mismatch task, we trained a long short term memory (LSTM) based model to determine which of two candidate speech segments matches with a given EEG segment. Main results. Our results show that vowel-consonant onsets outperform onsets of any phone in both tasks, which suggests that neural tracking of the vowel vs. consonant exists in the EEG to some degree. We also observed that vowel (syllable nucleus) onsets exhibit a more consistent representation in EEG compared to syllable onsets. Significance. Finally, our findings suggest that neural tracking previously thought to be associated with broad phonetic classes might actually originate from vowel-consonant onsets rather than the differentiation between different phonetic classes.
目的。为了研究听觉系统如何处理自然语音,我们建立了一些模型,将听语音的人的脑电图(EEG)信号与语音的各种表征联系起来。主要使用的是语音包络线,但也使用了语音表征。我们研究了语音表征与脑电信号的关联粒度。研究方法我们使用了 105 名受试者聆听童话故事时录制的脑电信号。我们利用语音表征,包括任何电话的起始音、元音-辅音起始音、广义语音类(BPC)起始音和狭义语音类(NPC)起始音,并通过前向建模和匹配-错配任务将它们与脑电图联系起来。在前向建模中,我们使用线性模型根据语音表征预测脑电图。在匹配-错配任务中,我们训练了一个基于长短期记忆(LSTM)的模型,以确定两个候选语音片段中哪一个与给定的脑电图片段相匹配。主要结果我们的结果表明,在这两项任务中,元音与辅音的起音效果优于任何音调的起音效果,这表明脑电图在一定程度上存在对元音与辅音的神经跟踪。我们还观察到,元音(音节核)母音在脑电图中的表现比音节母音更一致。意义最后,我们的研究结果表明,以前认为与宽泛的语音类别相关的神经跟踪实际上可能源自元音-辅音的起始,而不是不同语音类别之间的区分。
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引用次数: 0
Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding. Jump-GRS:用于神经解码的神经网络结构化剪枝的多阶段方法。
IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-07-31 DOI: 10.1088/1741-2552/ace5dc
Xiaomin Wu, Da-Ting Lin, Rong Chen, Shuvra S Bhattacharyya

Objective.Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming increasingly popular in many application fields of machine learning, show promising performance in neural decoding compared to traditional neural decoding methods. Various neural decoding applications, such as brain computer interface applications, require both high decoding accuracy and real-time decoding speed. Pruning methods are used to produce compact DNN models for faster computational speed. Greedy inter-layer order with Random Selection (GRS) is a recently-designed structured pruning method that derives compact DNN models for calcium-imaging-based neural decoding. Although GRS has advantages in terms of detailed structure analysis and consideration of both learned information and model structure during the pruning process, the method is very computationally intensive, and is not feasible when large-scale DNN models need to be pruned within typical constraints on time and computational resources. Large-scale DNN models arise in neural decoding when large numbers of neurons are involved. In this paper, we build on GRS to develop a new structured pruning algorithm called jump GRS (JGRS) that is designed to efficiently compress large-scale DNN models.Approach.On top of GRS, JGRS implements a 'jump mechanism', which bypasses retraining intermediate models when model accuracy is relatively less sensitive to pruning operations. Design of the jump mechanism is motivated by identifying different phases of the structured pruning process, where retraining can be done infrequently in earlier phases without sacrificing accuracy. The jump mechanism helps to significantly speed up execution of the pruning process and greatly enhance its scalability. We compare the pruning performance and speed of JGRS and GRS with extensive experiments in the context of neural decoding.Main results.Our results demonstrate that JGRS provides significantly faster pruning speed compared to GRS, and at the same time, JGRS provides pruned models that are similarly compact as those generated by GRS.Significance.In our experiments, we demonstrate that JGRS achieves on average 9%-20% more compressed models compared to GRS with 2-8 times faster speed (less time required for pruning) across four different initial models on a relevant dataset for neural data analysis.

目的:神经解码是神经工程学的一个重要领域,有助于将神经活动与行为联系起来。深度神经网络(DNN)在机器学习的许多应用领域越来越受欢迎,与传统的神经解码方法相比,深度神经网络在神经解码方面表现出良好的性能。各种神经解码应用,如脑计算机接口应用,都需要高解码精度和实时解码速度。剪枝方法用于生成紧凑的 DNN 模型,以提高计算速度。具有随机选择功能的贪婪层间顺序(GRS)是一种最新设计的结构化剪枝方法,可为基于钙成像的神经解码生成紧凑的 DNN 模型。虽然 GRS 在剪枝过程中具有详细的结构分析和同时考虑学习信息和模型结构的优势,但该方法的计算量非常大,当需要在典型的时间和计算资源限制下剪枝大规模 DNN 模型时,该方法并不可行。当神经解码涉及大量神经元时,就会出现大规模 DNN 模型。在本文中,我们在 GRS 的基础上开发了一种新的结构化剪枝算法,称为跳转 GRS(JGRS),旨在有效压缩大规模 DNN 模型。跳转机制的设计动机在于确定结构化剪枝过程的不同阶段,在这些阶段中,可以在不影响准确性的情况下,在早期阶段不频繁地进行重新训练。跳转机制有助于显著加快剪枝过程的执行速度,并大大提高其可扩展性。我们以神经解码为背景,通过大量实验比较了 JGRS 和 GRS 的剪枝性能和速度。主要结果:我们的结果表明,与 GRS 相比,JGRS 的剪枝速度明显更快,同时,JGRS 提供的剪枝模型与 GRS 生成的模型同样紧凑。在我们的实验中,我们证明了与 GRS 相比,JGRS 在神经数据分析的相关数据集上的四个不同的初始模型中,平均压缩了 9%-20% 的模型,速度提高了 2-8 倍(剪枝所需的时间更少)。
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引用次数: 0
A sub-region combination scheme for spatial coding in a high-frequency SSVEP-based BCI. 基于ssvep的高频脑机接口空间编码的子区域组合方案。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-07-27 DOI: 10.1088/1741-2552/ace8bd
Ruochen Hu, Gege Ming, Yijun Wang, Xiaorong Gao

Objective.In studying the spatial coding mechanism of visual evoked potentials, it is significant to construct a model that shows the relationship between steady-state visual evoked potential (SSVEP) responses to the local and global visual field stimulation. In order to investigate whether SSVEPs produced by sub-region stimulation can predict that produced by joint region stimulation, a sub-region combination scheme for spatial coding in a high-frequency SSVEP-based brain-computer interface (BCI) is developed innovatively.Approach.An annular visual field is divided equally into eight sub-regions. The 60 Hz visual stimuli in different sub-regions and joint regions are presented separately to participants. The SSVEP produced by the sub-region stimulation is superimposed to simulate the SSVEP produced by the joint region stimulation with different spatial combinations. A four-class spatially-coded BCI paradigm is used to evaluate the simulated classification performance, and the performance ranking of all simulated SSVEPs is obtained. Six representative stimulus patterns from two performance levels and three stimulus areas are applied to the online BCI system for each participant.Main results.The experimental result shows that the proposed scheme can implement a spatially-coded visual BCI system and realize satisfactory performance with imperceptible flicker. Offline analysis indicates that the classification accuracy and information transfer rate (ITR) are 89.69 ± 8.75% and 24.35 ± 7.09 bits min-1with 3 s data length under the 3/8 stimulus area. The online BCI system reaches an average classification accuracy of 87.50 ± 9.13% with 3 s data length, resulting in an ITR of 22.48 ± 6.71 bits min-1under the 3/8 stimulus area.Significance.This study proves the feasibility of using the sub-region's response to predict the joint region's response. It has the potential to extend to other frequency bands and lays a foundation for future research on more complex spatial coding methods.

目标。在研究视觉诱发电位的空间编码机制中,建立稳态视觉诱发电位(SSVEP)响应局部和全局视野刺激的关系模型具有重要意义。为了研究子区域刺激产生的ssvep是否能预测联合区域刺激产生的ssvep,创新性地提出了一种基于高频ssvep的脑机接口(BCI)空间编码的子区域组合方案。不同子区域和关节区域的60 Hz视觉刺激分别呈现给被试。将子区域刺激产生的SSVEP进行叠加,模拟不同空间组合的联合区域刺激产生的SSVEP。采用四类空间编码BCI范式对模拟的分类性能进行了评价,得到了所有模拟的ssvep的性能排名。从两个表现水平和三个刺激区域中选取六种具有代表性的刺激模式应用于每个参与者的在线BCI系统。主要的结果。实验结果表明,该方案能够实现空间编码的视觉BCI系统,并在闪烁不明显的情况下实现令人满意的性能。离线分析表明,在3/8刺激区域下,当数据长度为3 s时,分类准确率为89.69±8.75%,信息传输率(ITR)为24.35±7.09 bits min-1。该在线BCI系统在数据长度为3 s的情况下,平均分类准确率达到87.50±9.13%,在3/8刺激区域下的ITR为22.48±6.71 bits min-1。它具有扩展到其他频段的潜力,为未来更复杂的空间编码方法的研究奠定了基础。
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引用次数: 0
Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production. 在无声语音产生过程中,从人类皮层表面记录中连续合成人工语音。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-07-27 DOI: 10.1088/1741-2552/ace7f6
Kevin Meng, Farhad Goodarzy, EuiYoung Kim, Ye Jin Park, June Sic Kim, Mark J Cook, Chun Kee Chung, David B Grayden

Objective. Brain-computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.Approach. Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments.Main results. Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by naïve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech.Significance.As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.

目标。脑机接口可以恢复瘫痪患者各种形式的交流,这些瘫痪患者已经失去了清晰的语言能力。本研究旨在证明在无声语音产生过程中从人类皮层表面记录中闭环合成人工语音的可行性。10名患有顽固性癫痫的参与者在大脑皮层表面暂时植入了颅电极阵列。在显性单词阅读过程中,对解码模型进行了训练,该模型直接预测了患者特定神经特征输入的可听输出,并立即对显性、模仿和想象的单词阅读进行了测试。预测的输出随后会根据相应的录音进行客观评估,并通过人类的感知判断进行主观评估。主要的结果。人工语音成功地合成在公开和隐晦的话语由两个参与者与一些覆盖中央前回。在两种选择的强迫选择任务中,naïve的听者正确识别了大约三分之一的声音。在任何参与者想象的话语中都无法达到类似的结果。然而,神经特征贡献分析表明,在想象言语过程中,中枢后回和颞上回存在可利用的激活模式。在未来的研究中,更全面地覆盖皮层表面,包括额中回和额下回的后部,可以提高想象语音中的合成性能。意义随着言语神经假肢领域迅速走向临床试验,本研究在对非目标参与者进行无声语言研究时,解决了任务指令和大脑覆盖的重要问题。
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引用次数: 0
Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions. 超快超声波的空间重复性成分与人体等长收缩时的运动单元活动有关。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-07-26 DOI: 10.1088/1741-2552/ace6fc
Robin Rohlén, Marco Carbonaro, Giacinto L Cerone, Kristen M Meiburger, Alberto Botter, Christer Grönlund

Objective.Ultrafast ultrasound (UUS) imaging has been used to detect intramuscular mechanical dynamics associated with single motor units (MUs). Detecting MUs from ultrasound sequences requires decomposing a velocity field into components, each consisting of an image and a signal. These components can be associated with putative MU activity or spurious movements (noise). The differentiation between putative MUs and noise has been accomplished by comparing the signals with MU firings obtained from needle electromyography (EMG). Here, we examined whether the repeatability of the images over brief time intervals can serve as a criterion for distinguishing putative MUs from noise in low-force isometric contractions.Approach.UUS images and high-density surface EMG (HDsEMG) were recorded simultaneously from 99 MUs in the biceps brachii of five healthy subjects. The MUs identified through HDsEMG decomposition were used as a reference to assess the outcomes of the ultrasound-based components. For each contraction, velocity sequences from the same eight-second ultrasound recording were separated into consecutive two-second epochs and decomposed. To evaluate the repeatability of components' images across epochs, we calculated the Jaccard similarity coefficient (JSC). JSC compares the similarity between two images providing values between 0 and 1. Finally, the association between the components and the MUs from HDsEMG was assessed.Main results.All the MU-matched components had JSC > 0.38, indicating they were repeatable and accounted for about one-third of the HDsEMG-detected MUs (1.8 ± 1.6 matches over 4.9 ± 1.8 MUs). The repeatable components (JSC > 0.38) represented 14% of the total components (6.5 ± 3.3 components). These findings align with our hypothesis that intra-sequence repeatability can differentiate putative MUs from noise and can be used for data reduction.Significance.This study provides the foundation for developing stand-alone methods to identify MU in UUS sequences and towards real-time imaging of MUs. These methods are relevant for studying muscle neuromechanics and designing novel neural interfaces.

目的:超快超声(UUS)成像已被用于检测与单个运动单元(MU)相关的肌肉内机械动力学。从超声波序列中检测单个运动单元需要将速度场分解为多个分量,每个分量由图像和信号组成。这些成分可能与假定的运动单元活动或虚假运动(噪音)有关。通过将信号与针刺肌电图(EMG)获得的 MU 发火进行比较,可以区分假定的 MU 和噪声。在此,我们研究了图像在短暂时间间隔内的可重复性是否可作为区分低力等长收缩中推定 MU 和噪声的标准。方法:UUS 图像和高密度表面肌电图(HDsEMG)同时记录了 5 名健康受试者肱二头肌中 99 个 MU 的活动。通过 HDsEMG 分解确定的 MU 被用作评估超声组件结果的参考。对于每次收缩,来自同一八秒超声波记录的速度序列被分离成连续的两秒时程并进行分解。为了评估组件图像在不同时间段的重复性,我们计算了 Jaccard 相似系数 (JSC)。主要结果:所有与 MU 匹配的成分的 JSC 均大于 0.38,表明它们具有可重复性,并占 HDsEMG 检测到的 MU 的三分之一(1.8 ± 1.6 个匹配,4.9 ± 1.8 个 MU)。可重复成分(JSC > 0.38)占总成分的 14%(6.5 ± 3.3 个成分)。这些发现与我们的假设一致,即序列内重复性可将推定的MU从噪声中区分出来,并可用于减少数据。这项研究为开发独立的方法来识别UUS序列中的MU以及对MU进行实时成像奠定了基础。这些方法与研究肌肉神经力学和设计新型神经接口息息相关。
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引用次数: 0
Thermal safety considerations for implantable micro-coil design. 植入式微型线圈设计的热安全考虑因素。
IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-07-26 DOI: 10.1088/1741-2552/ace79a
Andrew J Whalen, Shelley I Fried

Micro magnetic stimulation of the brain via implantable micro-coils is a promising novel technology for neuromodulation. Careful consideration of the thermodynamic profile of such devices is necessary for effective and safe designs.Objective.We seek to quantify the thermal profile of bent wire micro-coils in order to understand and mitigate thermal impacts of micro-coil stimulation.Approach. In this study, we use fine wire thermocouples and COMSOL finite element modeling to examine the profile of the thermal gradients generated near bent wire micro-coils submerged in a water bath during stimulation. We tested a range of stimulation parameters previously reported in the literature such as voltage amplitude, stimulus frequency, stimulus repetition rate and coil wire materials.Main results. We found temperature increases ranging from <1 °C to 8.4 °C depending upon the stimulation parameters tested and coil wire materials used. Numerical modeling of the thermodynamics identified hot spots of the highest temperatures along the micro-coil contributing to the thermal gradients and demonstrated that these thermal gradients can be mitigated by the choice of wire conductor material and construction geometry.Significance. ISO standard 14708-1 designates a thermal safety limit of 2 °C temperature increase for active implantable medical devices. By switching the coil wire material from platinum/iridium to gold, our study achieved a 5-6-fold decrease in the thermal impact of coil stimulation. The thermal gradients generated from the gold wire coil were measured below the 2 °C safety limit for all stimulation parameters tested.

通过植入式微线圈对大脑进行微磁刺激是一种很有前途的神经调控新技术。我们试图量化弯曲导线微线圈的热曲线,以了解和减轻微线圈刺激的热影响。在这项研究中,我们使用细线热电偶和 COMSOL 有限元建模来研究浸没在水浴中的弯曲金属丝微线圈在刺激过程中产生的热梯度曲线。我们测试了之前文献中报道的一系列刺激参数,如电压幅度、刺激频率、刺激重复率和线圈丝材料。我们发现温度升高的幅度从 0.5 到 1.5 不等。ISO 标准 14708-1 规定,有源植入式医疗器械的热安全限值为温升 2°C。通过将线圈丝材料从铂/铱改为金,我们的研究发现线圈刺激的热影响降低了 5-6 倍。在所有测试的刺激参数中,金线线圈产生的热梯度均低于 2 °C 的安全限值。
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Journal of neural engineering
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