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Geometric neural network based on phase space for BCI-EEG decoding. 基于相空间的几何神经网络用于 BCI-EEG 解码。
Pub Date : 2024-10-18 DOI: 10.1088/1741-2552/ad88a2
Igor Carrara, Bruno Aristimunha, Marie-Constance Corsi, Raphael Yokoingawa de Camargo, Sylvain Chevallier, Theodore Papadopoulo

textbf{Objective:} The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for BCI, where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography (EEG) is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes. textbf{Approach:} Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the method{} architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark (MOABB) framework. textbf{Main results:} The results of our method{} demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. textbf{Significance:} This new architecture is explainable and with a low number of trainable parameters.

textbf{Objective:} 与计算机视觉等领域的成功相比,深度学习(DL)算法与大脑信号分析的整合仍处于初级阶段。这一点在生物识别(BCI)领域尤为明显,在该领域,大脑活动被解码,从而无需肌肉控制即可控制外部设备。 脑电图(EEG)因其非侵入性、成本效益高以及出色的时间分辨率而被广泛用于设计生物识别(BCI)系统。然而,它的代价是训练数据有限、信噪比差、受试者之间和受试者内部记录差异大。最后,用许多电极建立一个 BCI 系统需要很长时间,这阻碍了可靠的 DL 架构在研究实验室以外的 BCIs 中的广泛应用。为了提高采用率,我们需要提高用户的舒适度,例如使用只需少量电极即可运行的可靠算法。我们的研究旨在开发一种DL算法,该算法能在电极数量有限的情况下提供有效的结果。利用增强协方差法和 SPDNet 框架的优势,我们提出了 method{} 架构,并分析了其性能和结果的可解释性。评估是在 5 倍交叉验证的基础上进行的,只使用了位于运动皮层上方的三个电极。该方法使用MOABB(Mother Of All BCI Benchmark)框架在多个开源数据集的近100名受试者身上进行了测试。我们的方法{}的结果表明,结合 SPDNet 的增强方法在 MI 解码方面明显优于当前所有最先进的 DL 架构。这种新架构是可解释的,而且可训练的参数数量较少。
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引用次数: 0
User-wise perturbations for user identity protection in EEG-based BCIs. 在基于脑电图的生物识别(BCI)系统中保护用户身份的用户自扰动。
Pub Date : 2024-10-18 DOI: 10.1088/1741-2552/ad88a5
Xiaoqing Chen, Siyang Li, Yunlu Tu, Ziwei Wang, Dongrui Wu

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., user identity, emotion, disorders, etc., which should be protected.

Approach: We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e., random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected.

Main results: Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations.

Significance: Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.

目的:基于脑电图(EEG)的脑机接口(BCI)是人脑与计算机之间的直接通信途径。迄今为止,大多数研究都在研究更精确的 BCI,但对 BCI 的伦理问题关注较少。除了特定任务信息外,脑电信号还包含丰富的私人信息,如用户身份、情绪、疾病等,这些信息都应受到保护:方法:我们首次证明,添加用户自发扰动可使脑电图中的身份信息变得不可学习。我们提出了四种用户明智的隐私保护扰动,即随机噪音、合成噪音、误差最小化噪音和误差最大化噪音。在脑电图训练数据中加入建议的扰动后,数据中的用户身份信息变得不可学习,而BCI任务信息则不受影响:主要结果:使用三种神经网络分类器和各种传统机器学习模型在六个脑电图数据集上进行的实验证明了所提出的扰动的鲁棒性和实用性:我们的研究表明,在不影响主要 BCI 任务信息的情况下,在脑电图数据中隐藏用户身份信息是可行的。
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引用次数: 0
Brain and Muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals. 为康复性生物识别(BCI)分辨简单手部运动任务的大脑和肌肉衍生特征:对健康人和中风后患者的比较研究。
Pub Date : 2024-10-17 DOI: 10.1088/1741-2552/ad8838
Valeria de Seta, Emma Colamarino, Floriana Pichiorri, Giulia Savina, Francesca Patarini, Angela Riccio, Febo Cincotti, Donatella Mattia, Jlenia Toppi

Objective: Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols. Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level. Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand. Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their affected hand. Interestingly, brain features performed better in this latter condition with respect to healthy subjects. Significance. Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.

目的:针对中风后上肢恢复的脑机接口主要采用脑电图来解码与运动相关的大脑激活。最近推出了包括肌肉活动在内的混合系统。我们比较了 13 名健康受试者和 13 名中风患者在执行/尝试上肢康复方案中常用的两项简单手部运动任务(伸指和抓握)时,三种不同特征的运动任务分辨能力,即作为脑源性特征的事件相关非同步化/同步化(ERD/ERS)和运动相关皮质电位(MRCP),以及作为脑-肌肉混合衍生特征的皮质-肌肉一致性(CMC)。我们采用了一种三向统计设计,以研究他们对这两种动作的分辨能力是否会随着动作执行过程的特定时间演变而变化,以及这三种特征之间和两组之间最终是否存在差异。我们还研究了单个被试水平上的表现差异。ERD/ERS 和基于 CMC 的分类表现出相似的时间变化,在动作执行阶段准确率显著提高,而基于 MRCP 的准确率在动作开始时达到峰值。中风患者在用患手尝试动作时,这种时间动态变化相似,但速度较慢。此外,健康受试者和中风患者在用未受影响的手执行任务时,CMC 的表现优于两种脑特征,而在用受影响的手执行任务时,受试者之间的变异性更高。有趣的是,脑特征在后一种情况下的表现优于健康受试者。我们的研究结果为改善脑卒中后康复的脑机接口设计提供了提示,强调了根据患者特征和预期康复目标量身定制个性化方法的必要性。
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引用次数: 0
A multi-network model of Parkinson's disease tremor: exploring the finger-dimmer-switch theory and role of dopamine in thalamic self-inhibition. 帕金森病震颤的多网络模型:探索手指调光开关理论和多巴胺在丘脑自我抑制中的作用。
Pub Date : 2024-10-16 DOI: 10.1088/1741-2552/ad7f8a
Fatemeh Sadeghi, Mariia Popova, Francisco Páscoa Dos Santos, Simone Zittel, Claus C Hilgetag

Background. Tremor is a cardinal symptom of Parkinson's disease (PD) that manifests itself through complex oscillatory activity across multiple neuronal populations. According to the finger-dimmer-switch (FDS) theory, tremor is triggered by transient pathological activity in the basal ganglia-thalamo-cortical (BTC) network (the finger) and transitions into an oscillatory form within the inner circuitry of the thalamus (the switch). The cerebello-thalamo-cortical (CTC) network (the dimmer) is then involved in sustaining and amplifying tremor amplitude. In this study, we aimed to investigate the generation and progression dynamics of PD tremor oscillations by developing a comprehensive and interacting FDS model that transitions sequentially from healthy to PD to tremor and then to tremor-off state.Methods.We constructed a computational model consisting of 700 neurons in 11 regions of BTC, CTC, and thalamic networks. Transition from healthy to PD state was simulated through modulating dopaminergic synaptic connections; and further from PD to tremor and tremor-off by modulating projections between the thalamic reticular nucleus (TRN), anterior ventrolateral nucleus (VLa), and posterior ventrolateral nucleus (VLp).Results.Sustained oscillations in the frequency range of PD tremor emerged in thalamic VLp (5 Hz) and cerebellar dentate nucleus (3 Hz). Increasing self-inhibition in the thalamus through dopaminergic modulation significantly decreased tremor amplitude.Conclusion/Significance.Our results confirm the mechanistic power of the FDS theory in describing the PD tremor phenomenon and emphasize the role of dopaminergic modulation on thalamic self-inhibition. These insights pave the way for novel therapeutic strategies aimed at reducing the tremor by strengthening thalamic self-inhibition, particularly in dopamine-resistant patients.

背景:震颤是帕金森病(PD)的主要症状,它通过多个神经元群的复杂振荡活动表现出来。根据 "手指-开关"(FDS)理论,震颤是由基底节-丘脑-皮层(BTC)网络(手指)中的短暂病理活动引发的,并在丘脑内部电路(开关)中转变为振荡形式。然后,小脑-丘脑-皮层(CTC)网络(调光器)参与维持和放大震颤幅度。在这项研究中,我们旨在通过建立一个从健康到帕金森病再到震颤再到震颤-关闭状态依次过渡的全面、相互作用的 FDS 模型,研究帕金森病震颤振荡的产生和发展动态:我们构建了一个由 BTC、CTC 和丘脑网络 11 个区域的 700 个神经元组成的计算模型。通过调节多巴胺能突触连接,模拟从健康状态到震颤状态的转变;通过调节丘脑网状核(TRN)、前腹外侧核(VLa)和后腹外侧核(VLp)之间的投射,模拟从震颤状态到震颤和震颤消失的转变:丘脑 VLp(5 Hz)和小脑齿状核(3 Hz)出现了与震颤麻痹症频率范围一致的持续振荡。通过多巴胺能调节增加丘脑的自我抑制可显著降低震颤幅度:我们的研究结果证实了 FDS 理论在描述帕金森病震颤现象方面的机理,并强调了多巴胺能调节丘脑自我抑制的作用。这些见解为旨在通过加强丘脑自我抑制来减轻震颤的新型治疗策略铺平了道路,尤其是在多巴胺耐药患者中。
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引用次数: 0
Technical survey of end-to-end signal processing in BCIs using invasive MEAs. 使用侵入式 MEA 的 BCI 端到端信号处理技术调查。
Pub Date : 2024-10-15 DOI: 10.1088/1741-2552/ad8031
Andreas Erbslöh, Leo Buron, Zia Ur-Rehman, Simon Musall, Camilla Hrycak, Philipp Löhler, Christian Klaes, Karsten Seidl, Gregor Schiele

Modern brain-computer interfaces and neural implants allow interaction between the tissue, the user and the environment, where people suffer from neurodegenerative diseases or injuries.This interaction can be achieved by using penetrating/invasive microelectrodes for extracellular recordings and stimulation, such as Utah or Michigan arrays. The application-specific signal processing of the extracellular recording enables the detection of interactions and enables user interaction. For example, it allows to read out movement intentions from recordings of brain signals for controlling a prosthesis or an exoskeleton. To enable this, computationally complex algorithms are used in research that cannot be executed on-chip or on embedded systems. Therefore, an optimization of the end-to-end processing pipeline, from the signal condition on the electrode array over the analog pre-processing to spike-sorting and finally the neural decoding process, is necessary for hardware inference in order to enable a local signal processing in real-time and to enable a compact system for achieving a high comfort level. This paper presents a survey of system architectures and algorithms for end-to-end signal processing pipelines of neural activity on the hardware of such neural devices, including (i) on-chip signal pre-processing, (ii) spike-sorting on-chip or on embedded hardware and (iii) neural decoding on workstations. A particular focus for the hardware implementation is on low-power electronic design and artifact-robust algorithms with low computational effort and very short latency. For this, current challenges and possible solutions with support of novel machine learning techniques are presented in brief. In addition, we describe our future vision for next-generation BCIs.

现代脑机接口和神经植入物可以让患有神经退行性疾病或受伤的组织、用户和环境进行互动。这种互动可以通过使用穿透性/侵入性微电极进行细胞外记录和刺激(如犹他或密歇根阵列)来实现。对细胞外记录进行特定应用信号处理,可以检测交互作用并实现用户交互。例如,它可以从大脑信号记录中读出运动意图,从而控制假肢或外骨骼。为此,研究中使用了计算复杂的算法,这些算法无法在芯片或嵌入式系统上执行。因此,从电极阵列上的信号条件到模拟预处理,再到尖峰排序,最后到神经解码过程,端到端处理流水线的优化对于硬件推理是必要的,这样才能实现实时的本地信号处理,并使系统结构紧凑,达到较高的舒适度。本文介绍了在此类神经设备硬件上对神经活动进行端到端信号处理流水线的系统架构和算法,包括:(i) 片上信号预处理;(ii) 片上或嵌入式硬件上的尖峰排序;(iii) 工作站上的神经解码。硬件实施的一个特别重点是低功耗电子设计,以及具有低计算量和极短延迟的人工智能算法。为此,我们简要介绍了当前面临的挑战以及在新型机器学习技术的支持下可能采取的解决方案。此外,我们还介绍了下一代生物识别(BCI)的未来愿景。
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引用次数: 0
The effects of neuron morphology and spatial distribution on the selectivity of dorsal root ganglion stimulation. 神经元形态和空间分布对刺激背根神经节选择性的影响
Pub Date : 2024-10-15 DOI: 10.1088/1741-2552/ad7760
Juhi Farooqui, Ameya C Nanivadekar, Marco Capogrosso, Scott F Lempka, Lee E Fisher

Objective.For prosthesis users, sensory feedback that appears to come from the missing limb can improve function, confidence, and phantom limb pain. Numerous pre-clinical studies have considered stimulation via penetrating microelectrodes at the dorsal root ganglion (DRG) as a potential approach for somatosensory neuroprostheses. However, to develop clinically translatable neuroprosthetic devices, a less invasive approach, such as stimulation via epineural macroelectrodes, would be preferable. This work explores the feasibility of using such electrodes to deliver focal sensory feedback by examining the mechanisms of selective activation in response to stimulation via epineural electrodes compared with penetrating electrodes.Approach.We developed computational models of the DRG, representing the biophysical properties of the DRG and surrounding tissue to evaluate neural responses to stimulation via penetrating microelectrodes and epineural macroelectrodes. To assess the role of properties such as neuron morphology and spatial arrangement we designed three models, including one that contained only axons (axon only), one with pseudounipolar neurons arranged randomly (random), and one with pseudounipolar neurons placed according to a realistic spatial distribution (realistic).Main results.Our models demonstrate that activation in response to stimulation via epineural electrodes in a realistic model is commonly initiated in the axon initial segment adjacent to the cell body, whereas penetrating electrodes commonly elicit responses in t-junctions and axons. Moreover, we see a wider dynamic range for epineural electrodes compared with penetrating electrodes. This difference appears to be driven by the spatial organization and neuron morphology of the realistic DRG.Significance.We demonstrate that the anatomical features of the DRG make it a potentially effective target for epineural stimulation to deliver focal sensations from the limbs. Specifically, we show that epineural stimulation at the DRG can be highly selective thanks to the neuroanatomical arrangement of the DRG, making this a promising approach for future neuroprosthetic development.

目的: 对于假肢使用者来说,来自缺失肢体的感觉反馈可以改善他们的功能、信心和幻肢痛。许多临床前研究都认为,通过穿透背根神经节(DRG)的微电极进行刺激是躯体感觉神经义肢的一种潜在方法。然而,要开发可应用于临床的神经假体设备,最好采用创伤较小的方法,如通过会神经大电极进行刺激。与穿透性电极相比,本研究通过研究神经外膜电极对刺激的选择性激活机制,探讨了使用此类电极提供局灶感觉反馈的可行性。为了评估神经元形态和空间排列等特性的作用,我们设计了三个模型,包括一个只包含轴突的模型(只包含轴突)、一个随机排列假双极神经元的模型(随机)和一个按照现实空间分布放置假双极神经元的模型(现实)。 主要结果: 我们的模型表明,在现实模型中,通过会神经电极刺激产生的激活反应通常是在邻近细胞体的轴突起始节段开始的,而穿透电极通常会在 T 型接头和轴突中引起反应。此外,与穿透电极相比,我们发现会神经电极的动态范围更广。这种差异似乎是由现实中 DRG 的空间组织和神经元形态决定的:我们证明,DRG 的解剖学特征使其有可能成为神经外膜刺激的有效目标,从而传递来自四肢的病灶感觉。具体来说,我们表明,由于 DRG 的神经解剖学排列,对 DRG 的神经外膜刺激具有高度选择性,这使其成为未来神经假体开发的一种有前途的方法。
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引用次数: 0
Task-guided attention increases non-linearity of steady-state visually evoked potentials. 任务引导的注意力会增加稳态视觉诱发电位的非线性。
Pub Date : 2024-10-15 DOI: 10.1088/1741-2552/ad8032
Asaf Harel, Oren Shriki

Objective.Attention is a multifaceted cognitive process, with nonlinear dynamics playing a crucial role. We investigated the involvement of nonlinear processes in top-down visual attention.Approach.The research paradigm employed a contrast-modulated sequence of letters and numerals, encircled by a consistently flickering white square on a black background-a setup that generated steady-state visually evoked potentials. Nonlinear processes are recognized for eliciting and modulating the harmonics of constant frequencies. Using the rhythmic entrainment source separation technique, we examined the fundamental and harmonic frequencies of each stimulus to evaluate the underlying nonlinear dynamics during stimulus processing.Main results.In line with prior research, our findings indicate that the power spectrum density of electroencephalogram responses is influenced by both task presence and stimulus contrast. We discovered that actively searching for a target within a letter stream heightened the amplitude of the fundamental frequency and harmonics related to the background flickering stimulus. While the fundamental frequency amplitude remained unaffected by the stimulus contrast, a lower contrast led to an increase in the second harmonic's amplitude. We assessed the relationship between the contrast response function and the nonlinear-based harmonic responses.Significance.Our findings contribute to a more nuanced understanding of the nonlinear processes impacting top-down visual attention.

注意力是一个多方面的认知过程,其中非线性动态过程起着至关重要的作用。在这项研究中,我们采用了一个对比度调制的字母和数字序列,其周围是黑底上持续闪烁的白色方块--这种设置能产生稳态视觉诱发电位,从而研究了非线性过程在自上而下视觉注意中的参与情况。非线性过程被认为可以激发和调制恒定频率的谐波。我们检查了每个刺激的基频和谐波频率,以评估刺激处理过程中潜在的非线性动态。与之前的研究一致,我们的研究结果表明,脑电图反应的功率谱密度受到任务存在和刺激对比度的影响。通过使用节奏性伴音源分离(RESS)技术,我们发现在字母流中主动搜索目标会提高基频和与背景闪烁刺激相关的谐波的振幅。虽然基频振幅不受刺激对比度的影响,但对比度越低,二次谐波的振幅就越大。我们评估了对比度反应函数与基于非线性的谐波反应之间的关系。我们的研究结果有助于人们更细致地了解影响自上而下视觉注意力的非线性过程,同时也为优化脑机接口提供了启示。
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引用次数: 0
A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications. 基于脑电图的应用中的进化算法和元启发式算法综合调查。
Pub Date : 2024-10-11 DOI: 10.1088/1741-2552/ad7f8e
Muhammad Arif, Faizan Ur Rehman, Lukas Sekanina, Aamir Saeed Malik

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

脑电图(EEG)已成为了解人类大脑复杂运作的主要非侵入性移动模式,为认知过程、神经疾病和脑机接口(BCI)提供了宝贵的见解。然而,脑电图数据量大、存在伪影、需要选择最佳通道以及需要从脑电图数据中提取特征,这些都给用于处理脑电图数据的机器学习算法带来了巨大挑战,使其难以获得有意义和有区别的结果。因此,为了有效克服这些障碍,对复杂优化技术的需求已变得势在必行。近年来,进化算法(EAs)和其他受自然启发的元启发式算法已被用作强大的设计和优化工具,在解决与基于脑 EEG 的应用相关的各种设计和优化问题方面展示了其重要意义。本文全面介绍了 EA 和其他元启发式算法在基于脑电图的应用中的重要性。调查按照 EAs 已应用的主要领域进行组织,即伪影缓解、通道选择、特征提取、特征选择和信号分类。最后,讨论了基于脑电图的应用中 EAs 目前面临的挑战和未来的发展方向。
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引用次数: 0
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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Journal of neural engineering
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