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Influence of feedback transparency on motor imagery neurofeedback performance: the contribution of agency. 反馈透明度对运动想象神经反馈表现的影响:代理的贡献。
Pub Date : 2024-10-09 DOI: 10.1088/1741-2552/ad7f88
Claire Dussard, Léa Pillette, Cassandra Dumas, Emeline Pierrieau, Laurent Hugueville, Brian Lau, Camille Jeunet-Kelway, Nathalie George

Objective.Neurofeedback (NF) is a cognitive training procedure based on real-time feedback (FB) of a participant's brain activity that they must learn to self-regulate. A classical visual FB delivered in a NF task is a filling gauge reflecting a measure of brain activity. This abstract visual FB is not transparently linked-from the subject's perspective-to the task performed (e.g., motor imagery (MI)). This may decrease the sense of agency, that is, the participants' reported control over FB. Here, we assessed the influence of FB transparency on NF performance and the role of agency in this relationship.Approach.Participants performed a NF task using MI to regulate brain activity measured using electroencephalography. In separate blocks, participants experienced three different conditions designed to vary transparency: FB was presented as either (1) a swinging pendulum, (2) a clenching virtual hand, (3) a clenching virtual hand combined with a motor illusion induced by tendon vibration. We measured self-reported agency and user experience after each NF block.Main results. We found that FB transparency influences NF performance. Transparent visual FB provided by the virtual hand resulted in significantly better NF performance than the abstract FB of the pendulum. Surprisingly, adding a motor illusion to the virtual hand significantly decreased performance relative to the virtual hand alone. When introduced in incremental linear mixed effect models, self-reported agency was significantly associated with NF performance and it captured the variance related to the effect of FB transparency on NF performance.Significance. Our results highlight the relevance of transparent FB in relation to the sense of agency. This is likely an important consideration in designing FB to improve NF performance and learning outcomes.

目的:神经反馈(NF)是一种基于参与者大脑活动实时反馈(FB)的认知训练程序,参与者必须学会自我调节。神经反馈任务中提供的经典视觉 FB 是一个反映大脑活动测量值的填充量表。从受试者的角度来看,这种抽象的视觉 FB 与所执行的任务(如运动想象)之间没有透明的联系。这可能会降低被试的代入感,也就是被试对 FB 的控制能力。在这里,我们评估了FB透明度对NF表现的影响以及代理在这种关系中的作用。参与者利用运动想象来执行一项 NF 任务,并通过脑电图来调节大脑活动。在不同的区块中,受试者经历了三种旨在改变透明度的不同条件:FB表现为:1)摆动的钟摆;2)紧握的虚拟手;3)紧握的虚拟手与肌腱振动引起的运动幻觉相结合。我们在每个 NF 块后测量了自我报告的代理和用户体验。我们发现 FB 的透明度会影响 NF 的表现。虚拟手提供的透明视觉 FB 比摆锤的抽象 FB 的 NF 表现要好得多。令人惊讶的是,在虚拟手的基础上添加运动幻觉会明显降低NF成绩,而虚拟手则不会。当引入增量线性混合效应模型时,自我报告的代理与NF表现显著相关,它捕捉到了与FB透明度对NF表现的影响相关的变异。我们的研究结果凸显了透明的财务报告与代理感的相关性。这可能是设计FB以提高NF绩效和学习成果的一个重要考虑因素。
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引用次数: 0
Simultaneous modulation of pulse charge and burst period elicits two differentiable referred sensations. 同时调制脉冲电荷和脉冲串周期可产生两种不同的感觉。
Pub Date : 2024-10-08 DOI: 10.1088/1741-2552/ad7f8c
T R Benigni, A E Pena, S S Kuntaegowdanahalli, J J Abbas, R Jung

Objective.To investigate the feasibility of delivering multidimensional feedback using a single channel of peripheral nerve stimulation by complementing intensity percepts with flutter frequency percepts controlled by burst period modulation.Approach.Two dimensions of a distally referred sensation were provided simultaneously: intensity was conveyed by the modulation of the pulse charge rate inside short discrete periods of stimulation referred to as bursts and frequency was conveyed by the modulation of the period between bursts. For this approach to be feasible, intensity percepts must be perceived independently of frequency percepts. Two experiments investigated these interactions. A series of two alternative forced choice tasks (2AFC) were used to investigate burst period modulation's role in intensity discernibility. Magnitude estimation tasks were used to determine any interactions in the gradation between the frequency and intensity percepts.Main results.The 2AFC revealed that burst periods can be individually differentiated as a gradable frequency percept in peripheral nerve stimulation. Participants could correctly rate a perceptual scale of intensity and frequency regardless of the value of the second, but the dependence of frequency differentiability on charge rate indicates that frequency was harder to detect with weaker intensity percepts. The same was not observed in intensity differentiability as the length of burst periods did not significantly alter intensity differentiation. These results suggest multidimensional encoding is a promising approach for increasing information throughput in sensory feedback systems if intensity ranges are selected properly.Significance.This study offers valuable insights into haptic feedback through the peripheral nervous system and demonstrates an encoding approach for neural stimulation that may offer enhanced information transfer in virtual reality applications and sensory-enabled prosthetic systems. This multidimensional encoding strategy for sensory feedback may open new avenues for enriched control capabilities.

目的 研究使用单通道外周神经刺激提供多维反馈的可行性,方法是通过突发周期调制控制的扑动频率感知来补充强度感知。 方法 同时提供远端指涉感觉的两个维度:强度通过调制脉冲电荷率传递给短的离散刺激周期(称为突发),频率通过调制突发之间的周期传递。要使这种方法可行,强度感知必须独立于频率感知。有两个实验对这些相互作用进行了研究。我们使用了一系列两组强迫选择任务(2AFC)来研究脉冲串周期调制在强度可辨性中的作用。幅值估计任务用于确定频率和强度感知之间渐变的交互作用。 2AFC显示,在周围神经刺激中,突发周期可以作为一种可渐变的频率感知单独区分出来。无论第二项的数值如何,参与者都能正确评定强度和频率的知觉等级,但频率可区分性与电荷率的关系表明,强度知觉较弱时,频率较难检测到。在强度可区分性方面没有观察到同样的情况,因为脉冲串周期的长度并不会显著改变强度可区分性。这些结果表明,如果强度范围选择得当,多维编码是在感觉反馈系统中提高信息吞吐量的一种有前途的方法。这种感官反馈的多维编码策略可能会为丰富控制能力开辟新的途径。
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引用次数: 0
Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data. 纵向数据的多元宇宙分析中,感知运动脑机接口的性能取决于信噪比,而不取决于μ节律的连通性。
Pub Date : 2024-10-08 DOI: 10.1088/1741-2552/ad7a24
Nikolai Kapralov, Mina Jamshidi Idaji, Tilman Stephani, Alina Studenova, Carmen Vidaurre, Tomas Ros, Arno Villringer, Vadim Nikulin

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

目的:感知运动脑机接口(BCI)可作为与闭锁病人交流或控制假肢的渠道,它能从记录的使用者大脑活动中解码想象中的动作。然而,许多人仍然无法控制BCI,其潜在机制也不清楚。之前的研究表明,用户的 BCI 性能与μ节律的静息态信噪比(SNR)和感觉运动区之间μ节律的相位同步(PS)相关。然而,这些性能预测因素主要是在单次 BCI 会话中进行评估的,而纵向方面仍未得到研究。此外,不同的分析管道被用于估算源空间中的PS,这可能会妨碍结果的可重复性:为了系统地解决这些问题,我们使用一个公开的数据集对刺激前信噪比、PS 和会话中的 BCI 性能之间的关系进行了广泛的验证,该数据集包含 62 名进行了多达 11 次 BCI 训练的人类参与者。我们使用表面拉普拉斯在传感器空间进行了分析,并在多重宇宙分析中结合 24 个处理管道在源空间进行了分析。通过这种方法,我们可以研究观察到的效果对管道选择的稳健性:主要结果:我们的研究结果表明,对于大多数管道而言,信噪比对 BCI 性能既有受试者间的影响,也有受试者内的影响。相比之下,PS 对 BCI 性能的影响对管道选择的稳健性较差,在控制 SNR 后变得不显著:综上所述,我们的研究结果表明,感觉运动系统内神经元连接的变化对于学习控制BCI并不重要,而提高μ节奏信噪比的干预措施可能会提高用户的BCI性能。
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引用次数: 0
E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks. E-SAT:基于极端学习机的自我关注方法,用于解码受试者特定任务中的运动图像脑电图。
Pub Date : 2024-10-07 DOI: 10.1088/1741-2552/ad83f4
Muhammad Ahmed Ahmed Abbasi, Hafza Faiza Abbasi, Xiaojun Yu, Muhammad Zulkifal Aziz, Nicole Tye June Yih Yih, Zeming Fan

The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .

脑机接口(BCI)技术的进步实现了人脑与外部外围设备之间的直接通信,从而极大地改善了人们的生活。近年来,机器学习(ML)和深度学习(DL)模型的集成大大提高了 BCI 解码运动图像(MI)任务的性能。然而,这些现有模型仍存在一些局限性,例如训练时间长、对噪声或异常值的敏感性高,这在很大程度上阻碍了 BCI 的快速发展。为了解决这些问题,本文提出了一种新颖的基于极端学习机(ELM)的自我注意(E-SAT)机制,以提高针对特定对象的分类性能。具体而言,在 E-SAT 中,ELM 被用于提高自我注意模块在特征提取方面的泛化能力,以及优化模型的参数初始化过程。同时,还使用 ELM 对提取的特征进行分类,并使用基于 ELM 的端到端设置来评估 E-SAT 在不同 MI EEG 信号上的性能。通过对不同数据集(如 BCI Competition III 数据集 IV-a、IV-b 和 BCI Competition IV 数据集 1、2a、2b、3)进行广泛实验,验证了所提出的 E-SAT 策略的有效性。结果表明,在所有数据集上,E-SAT 的主题分类准确率分别达到 99.8%、99.1%、98.9%、75.8%、90.8% 和 95.4%,优于现有的几种最先进(SOTA)方法。实验结果不仅显示了 E-SAT 在特征提取方面的突出表现,还表明它有助于在其他九种鲁棒性特征提取中取得最佳结果。此外,本研究的结果还表明,E-SAT 在二元分类和多类分类任务中,以及在有噪声和无噪声数据集中都取得了卓越的性能。
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引用次数: 0
Development of a stereo-EEG based seizure matching system for clinical decision making in epilepsy surgery. 开发基于立体电子脑电图的癫痫发作匹配系统,用于癫痫手术的临床决策。
Pub Date : 2024-10-04 DOI: 10.1088/1741-2552/ad7323
John Thomas, Chifaou Abdallah, Kassem Jaber, Mays Khweileh, Olivier Aron, Irena Doležalová, Vadym Gnatkovsky, Daniel Mansilla, Päivi Nevalainen, Raluca Pana, Stephan Schuele, Jaysingh Singh, Ana Suller-Marti, Alexandra Urban, Jeffery Hall, François Dubeau, Louis Maillard, Philippe Kahane, Jean Gotman, Birgit Frauscher

Objective.The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity.Approach.We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity.Main results.The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75,p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers.Significance.We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.

目的:癫痫手术后不再发作的患者比例停滞不前。大型多中心立体脑电图数据集可将新患者与过去的类似病例进行比较,并在了解过去病例治疗方法的基础上做出临床决策。然而,这些评估的复杂性使得手动搜索类似患者变得不切实际。我们的目标是开发一种自动系统,从电学和解剖学角度将癫痫发作与数据库中的癫痫发作相匹配。此外,由于定义癫痫发作相似性的特征尚不清楚,我们评估了专家在分类相似性时的一致性和特征:我们利用了连续接受癫痫手术的 95 名患者的 320 次立体脑电图发作。八位国际专家使用四级相似性评分来评估发作对的相似性。作为主要结果,我们利用独立专家标记的患者数据开发并验证了自动癫痫发作匹配系统。次要结果包括评分者之间的一致性和癫痫发作相似性分类特征:癫痫发作匹配系统的中位曲线下面积为 0.76(四分位间范围为 0.1),表明其具有可行性。识别出并证明有效的六种不同的癫痫发作相似性特征:起始区域、起始模式、传播区域、持续时间、扩散范围和传播速度。在这些特征中,发病区域与专家评分的相关性最强(Spearman's rho=0.75,p
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引用次数: 0
Filter banks guided correlational convolutional neural network for SSVEPs based BCI classification. 滤波器组引导的相关卷积神经网络用于基于 SSVEPs 的 BCI 分类。
Pub Date : 2024-10-04 DOI: 10.1088/1741-2552/ad7f89
Xin Wen, Shuting Jia, Dan Han, Yanqing Dong, Chengxin Gao, Ruochen Cao, Yanrong Hao, Yuxiang Guo, Rui Cao

Objective.In the field of steady-state visual evoked potential brain computer interfaces (SSVEP-BCIs) research, convolutional neural networks (CNNs) have gradually been proved to be an effective method. Whereas, majority works apply the frequency domain characteristics in long time window to train the network, thus lead to insufficient performance of those networks in short time window. Furthermore, only the frequency domain information for classification lacks of other task-related information.Approach.To address these issues, we propose a time-frequency domain generalized filter-bank convolutional neural network (FBCNN-G) to improve the SSVEP-BCIs classification performance. The network integrates multiple frequency information of electroencephalogram (EEG) with template and predefined prior of sine-cosine signals to perform feature extraction, which contains correlation analyses in both template and signal aspects. Then the classification is performed at the end of the network. In addition, the method proposes the use of filter banks divided into specific frequency bands as pre-filters in the network to fully consider the fundamental and harmonic frequency characteristics of the signal.Main results.The proposed FBCNN-G model is compared with other methods on the public dataset Benchmark. The results manifest that this model has higher accuracy of character recognition accuracy and information transfer rates in several time windows. Particularly, in the 0.2 s time window, the mean accuracy of the proposed method reaches62.02%±5.12%, indicating its superior performance.Significance.The proposed FBCNN-G model is critical for the exploitation of SSVEP-BCIs character recognition models.

研究目的在稳态视觉诱发电位脑机接口(SSVEP-BCIs)研究领域,卷积神经网络(CNNs)逐渐被证明是一种有效的方法。然而,大多数研究都是利用长时间段内的频域特征来训练网络,从而导致这些网络在短时间内的性能不足。此外,仅利用频域信息进行分类缺乏与任务相关的其他信息:为解决这些问题,我们提出了一种时频域广义滤波器库卷积神经网络(FBCNN-G),以提高 SSVEP-BCI 的分类性能。该网络将脑电图(EEG)的多个频率信息与正弦波信号的模板和预设先验进行整合,以进行特征提取,其中包括模板和信号两方面的相关性分析。然后在网络末端进行分类。此外,该方法还建议在网络中使用按特定频段划分的滤波器组作为前置滤波器,以充分考虑信号的基频和谐波频率特性:主要结果:在公共数据集 Benchmark 上,将提出的 FBCNNG 模型与其他方法进行了比较。结果表明,在多个时间窗口中,该模型具有更高的字符识别精度和信息传输率。特别是在 0.2 秒的时间窗口中,所提出方法的平均准确率达到了 62.02 ± 5.12%,表明其性能优越。提出的 FBCNN-G 模型对于利用 SSVEP-BCI 字符识别模型至关重要。
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引用次数: 0
EEG electrodes and where to find them: automated localization from 3D scans. 脑电图电极及其位置:通过三维扫描自动定位。
Pub Date : 2024-09-30 DOI: 10.1088/1741-2552/ad7c7e
Mats Tveter, Thomas Tveitstøl, Tønnes Nygaard, Ana S Pérez T, Shrikanth Kulashekhar, Ricardo Bruña, Hugo L Hammer, Christoffer Hatlestad-Hall, Ira R J Hebold Haraldsen

Objective.The accurate localization of electroencephalography (EEG) electrode positions is crucial for accurate source localization. Recent advancements have proposed alternatives to labor-intensive, manual methods for spatial localization of the electrodes, employing technologies such as 3D scanning and laser scanning. These novel approaches often integrate magnetic resonance imaging (MRI) as part of the pipeline in localizing the electrodes. The limited global availability of MRI data restricts its use as a standard modality in several clinical scenarios. This limitation restricts the use of these advanced methods.Approach.In this paper, we present a novel, versatile approach that utilizes 3D scans to localize EEG electrode positions with high accuracy. Importantly, while our method can be integrated with MRI data if available, it is specifically designed to be highly effective even in the absence of MRI, thus expanding the potential for advanced EEG analysis in various resource-limited settings. Our solution implements a two-tiered approach involving landmark/fiducials localization and electrode localization, creating an end-to-end framework.Main results.The efficacy and robustness of our approach have been validated on an extensive dataset containing over 400 3D scans from 278 subjects. The framework identifies pre-auricular points and achieves correct electrode positioning accuracy in the range of 85.7% to 91.0%. Additionally, our framework includes a validation tool that permits manual adjustments and visual validation if required.Significance.This study represents, to the best of the authors' knowledge, the first validation of such a method on a substantial dataset, thus ensuring the robustness and generalizability of our innovative approach. Our findings focus on developing a solution that facilitates source localization, without the need for MRI, contributing to the critical discussion on balancing cost effectiveness with methodological accuracy to promote wider adoption in both research and clinical settings.

目的:准确定位脑电图(EEG)电极位置对于准确定位信号源至关重要。最近的进展提出了替代劳动密集型手工方法的电极空间定位方法,采用了三维扫描和激光扫描等技术。这些新方法通常将磁共振成像(MRI)作为电极定位管道的一部分。磁共振成像数据在全球范围内的可用性有限,这限制了它在一些临床场景中作为标准模式的使用。这种局限性限制了这些先进方法的使用:在本文中,我们提出了一种新颖的多功能方法,利用三维扫描高精度定位脑电图电极位置。重要的是,虽然我们的方法可以与核磁共振成像数据(如果有的话)结合使用,但它经过专门设计,即使在没有核磁共振成像的情况下也非常有效,从而扩大了在各种资源有限的环境中进行高级脑电图分析的潜力。我们的解决方案采用双层方法,包括地标/基底定位和电极定位,创建了一个端到端的框架:我们的方法的优越性和稳健性已在一个广泛的数据集上得到验证,该数据集包含来自 278 名受试者的 400 多张三维扫描图像。该框架能识别耳前点,电极定位的正确率在 85.7% 到 91.0% 之间。此外,我们的框架还包括一个验证工具,可根据需要进行手动调整和视觉验证:据作者所知,这项研究是首次在大量数据集上验证这种方法,从而确保了我们创新方法的稳健性和可推广性。我们的研究结果侧重于开发一种有助于来源定位的解决方案,有助于在成本效益与方法准确性之间取得平衡的重要讨论,从而促进在研究和临床环境中更广泛地采用这种方法。
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引用次数: 0
A shared robot control system combining augmented reality and motor imagery brain-computer interfaces with eye tracking. 将增强现实技术和运动图像脑机接口与眼动跟踪技术相结合的共享机器人控制系统。
Pub Date : 2024-09-25 DOI: 10.1088/1741-2552/ad7f8d
Arnau Dillen, Mohsen Omidi, Fakhreddine Ghaffari, Bram Vanderborght, Bart Roelands, Olivier Romain, Ann Nowé, Kevin De Pauw

Objective: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals. Approach: A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency. Main results:Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 minutes to complete the evaluation tasks. The success rate dropped below 0.5 when a 5-minute cutoff time was selected. Significance: These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications in the future.

目的:脑机接口(BCI)控制系统通过监测神经活动来检测用户的意图,从而通过心理想象来实现设备控制。尽管BCI具有很大的潜力,但在真实世界条件下对神经活动进行解码却面临着巨大的挑战,因此与传统的交互方法相比,BCI目前并不实用。本研究介绍了一种用于操作物理辅助机械臂的新型运动意象(MI)BCI 控制策略,解决了从脑电图(EEG)信号中解码运动意象的困难,因为脑电图信号本身是非稳态的,而且因人而异:利用市售硬件开发了一个概念验证 BCI 控制系统,在增强现实(AR)用户界面中将 MI 与眼动跟踪集成在一起,以促进共享控制方法。该系统根据用户的注视提出操作建议,使用户能够通过想象的动作进行选择。我们进行了一项用户研究,以评估该系统的可用性,重点关注其有效性和效率。主要结果:参与者用机械臂完成了模拟日常活动的任务,证明了共享控制系统在现实世界中的可行性和实用性。尽管在线解码性能较低(平均准确率:0.52 9,F1:0.29,Cohen's Kappa:0.12),但在用户研究的最后阶段,参与者在 15 分钟内完成评估任务的平均成功率达到了 0.83。当选择 5 分钟的截止时间时,成功率降至 0.5 以下:这些结果表明,尽管 MI-EEG 解码非常复杂,但将 AR 和眼动追踪整合在一起可以显著提高 BCI 系统的可用性。虽然效率仍然较低,但我们的方法的有效性得到了验证。这表明,未来生物识别(BCI)系统有可能成为日常应用中一种可行的交互模式 。
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引用次数: 0
Epidural Spinal Cord Recordings (ESRs): sources of neural-appearing artifact in stimulation evoked compound action potentials. 硬膜外脊髓记录(ESR):刺激诱发复合动作电位中神经显现伪影的来源。
Pub Date : 2024-09-25 DOI: 10.1088/1741-2552/ad7f8b
Ashlesha Deshmukh, Megan L Settell, Kevin Cheng, Bruce E Knudsen, James K Trevathan, Maria LaLuzerne, Stephan L Blanz, Aaron Skubal, Nishant Verma, Ben Benjamin Romanauski, Meagan K Brucker-Hahn, Danny Lam, Igor Lavrov, Aaron J Suminski, Douglas J Weber, Lee E Fisher, Scott F Lempka, Andrew J Shoffstall, Hyunjoo Park, Erika Ross, Mingming Zhang, Kip A Ludwig

Evoked compound action potentials (ECAPs) measured during epidural spinal cord stimulation (SCS) can help elucidate fundamental mechanisms for the treatment of pain and inform closed-loop control of SCS. Previous studies have used ECAPs to characterize neural responses to various neuromodulation therapies and have demonstrated that ECAPs are highly prone to multiple sources of artifact, including post-stimulus pulse capacitive artifact, electromyography (EMG) bleed-through, and motion artifact. However, a thorough characterization has yet to be performed for how these sources of artifact may contaminate recordings within the temporal window commonly used to determine activation of A-beta fibers in a large animal model. We characterized sources of artifacts that can contaminate the recording of ECAPs in an epidural SCS swine model using the Abbott Octrode™ lead. Spinal ECAP recordings can be contaminated by capacitive artifact, short latency EMG from nearby muscles of the back, and motion artifact. The capacitive artifact can appear nearly identical in duration and waveshape to evoked A-beta responses. EMG bleed-through can have phase shifts across the electrode array, similar to the phase shift anticipated by propagation of an evoked A-beta fiber response. The short latency EMG is often evident at currents similar to those needed to activate A-beta fibers associated with the treatment of pain. Changes in CSF between the cord and dura, and motion induced during breathing created a cyclic oscillation in all evoked components of recorded ECAPs. Controls must be implemented to separate neural signal from sources of artifact in SCS ECAPs. We suggest experimental procedures and reporting requirements necessary to disambiguate underlying neural response from these confounds. These data are important to better understand the framework for recorded ESRs, with components such as ECAPs, EMG, and artifacts, and have important implications for closed-loop control algorithms to account for transient motion such as postural changes and cough.

在硬膜外脊髓刺激(SCS)过程中测量的诱发复合动作电位(ECAP)有助于阐明治疗疼痛的基本机制,并为 SCS 的闭环控制提供信息。以往的研究利用 ECAPs 来描述神经对各种神经调控疗法的反应,结果表明 ECAPs 极易受到多种伪影来源的影响,包括刺激后脉冲电容伪影、肌电图 (EMG) 渗漏和运动伪影。我们利用雅培 Octrode™ 导联描述了在硬膜外 SCS 猪模型中记录 ECAP 时可能受到污染的伪影来源。脊髓 ECAP 记录可能受到电容伪影、来自背部附近肌肉的短潜伏 EMG 和运动伪影的污染。电容伪影的持续时间和波形与诱发的 A-beta 反应几乎相同。EMG 渗漏会在整个电极阵列中产生相移,类似于诱发 A-beta 纤维反应传播所预期的相移。短潜伏期 EMG 通常在电流与激活与疼痛治疗相关的 A-beta 纤维所需的电流相似时表现明显。脊髓和硬脊膜之间 CSF 的变化以及呼吸时引起的运动会在记录的 ECAP 的所有诱发成分中产生周期性振荡。我们提出了必要的实验程序和报告要求,以便将潜在的神经反应与这些干扰因素区分开来。这些数据对于更好地理解记录的 ESR(包括 ECAP、EMG 和伪影)的框架非常重要,并且对考虑瞬态运动(如姿势变化和咳嗽)的闭环控制算法具有重要意义。
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引用次数: 0
Nonlinear model predictive control of a conductance-based neuron model via data-driven forecasting. 通过数据驱动预测对基于传导的神经元模型进行非线性模型预测控制。
Pub Date : 2024-09-17 DOI: 10.1088/1741-2552/ad731f
Christof Fehrman, C Daniel Meliza

Objective. Precise control of neural systems is essential to experimental investigations of how the brain controls behavior and holds the potential for therapeutic manipulations to correct aberrant network states. Model predictive control, which employs a dynamical model of the system to find optimal control inputs, has promise for dealing with the nonlinear dynamics, high levels of exogenous noise, and limited information about unmeasured states and parameters that are common in a wide range of neural systems. However, the challenge still remains of selecting the right model, constraining its parameters, and synchronizing to the neural system.Approach. As a proof of principle, we used recent advances in data-driven forecasting to construct a nonlinear machine-learning model of a Hodgkin-Huxley type neuron when only the membrane voltage is observable and there are an unknown number of intrinsic currents.Main Results. We show that this approach is able to learn the dynamics of different neuron types and can be used with model predictive control (MPC) to force the neuron to engage in arbitrary, researcher-defined spiking behaviors.Significance.To the best of our knowledge, this is the first application of nonlinear MPC of a conductance-based model where there is only realistically limited information about unobservable states and parameters.

目的:神经系统的精确控制对于大脑如何控制行为的实验研究至关重要,同时也为纠正异常网络状态的治疗操作提供了可能性。模型预测控制利用系统的动力学模型来寻找最佳控制输入,有望处理非线性动力学、高水平的外源噪声以及有关未测量状态和参数的有限信息等问题,而这些问题在各种神经系统中十分常见。然而,如何选择合适的模型、限制其参数并与神经系统同步仍然是一个挑战:作为原理验证,我们利用数据驱动预测的最新进展,构建了一个霍奇金-赫胥黎型神经元的非线性机器学习模型:我们证明了这种方法能够学习不同神经元类型的动态,并能与 MPC 配合使用,迫使神经元参与研究人员定义的任意尖峰行为:据我们所知,这是基于电导模型的非线性 MPC 的首次应用,在这种模型中,不可观测的状态和参数信息非常有限。
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引用次数: 0
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Journal of neural engineering
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