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Adversarial artifact detection in EEG-based brain-computer interfaces. 基于脑电图的脑机接口中的对抗性伪影检测。
Pub Date : 2024-10-30 DOI: 10.1088/1741-2552/ad8964
Xiaoqing Chen, Lubin Meng, Yifan Xu, Dongrui Wu

Objective. machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detecting adversarial examples is crucial for both understanding this phenomenon and developing effective defense strategies.Approach. this paper, for the first time, explores adversarial detection in EEG-based BCIs. We extend several popular adversarial detection approaches from computer vision to BCIs. Two new Mahalanobis distance based adversarial detection approaches, and three cosine distance based adversarial detection approaches, are also proposed, which showed promising performance in detecting three kinds of white-box attacks.Main results. we evaluated the performance of eight adversarial detection approaches on three EEG datasets, three neural networks, and four types of adversarial attacks. Our approach achieved an area under the curve score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.Significance. through extensive experiments, we found that white-box attacks may be easily detected, and differences exist in the distributions of different types of adversarial examples. Our work should facilitate understanding the vulnerability of existing BCI models and developing more secure BCIs in the future.

目的:机器学习在基于脑电图(EEG)的脑机接口(BCI)方面取得了巨大成功,现有研究大多侧重于提高解码准确性。然而,最近的研究表明,基于脑电图的 BCI 很容易受到对抗性攻击的影响,在输入中添加的微小扰动会导致错误分类。检测对抗范例对于理解这一现象和制定有效的防御策略至关重要:本文首次探讨了基于脑电图的 BCI 中的对抗检测。我们将计算机视觉中几种流行的对抗检测方法扩展到了 BCI。我们还提出了两种新的基于马哈拉诺比斯距离的对抗检测方法和三种基于余弦距离的对抗检测方法,这些方法在检测三种白盒攻击方面表现出了良好的性能:我们在三个脑电图数据集、三个神经网络和四种对抗攻击中评估了八种对抗检测方法的性能。我们的方法在检测白盒攻击方面的曲线下面积(AUC)得分高达 99.99%。此外,我们还评估了不同对抗检测器对未知攻击的可转移性:通过大量实验,我们发现白盒攻击很容易被检测到,而且不同类型的对抗性实例的分布存在差异。我们的工作将有助于了解现有BCI模型的脆弱性,并在未来开发出更安全的BCI。
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引用次数: 0
Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG). 利用头皮脑电图(EEG)对公开语音进行连续和离散解码。
Pub Date : 2024-10-30 DOI: 10.1088/1741-2552/ad8d0a
Alexander Craik, Heather R Dial, Jose L Contreras-Vidal

Neurological disorders affecting speech production adversely impact quality of life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking devices and P300 spellers are slow and unnatural for these patients. An alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech characteristics, offering a more natural communication mechanism. This research explores the feasibility of decoding speech features using non-invasive EEG. Nine neurologically intact participants were equipped with a 63-channel EEG system with additional sensors to eliminate eye artifacts. Participants read aloud sentences displayed on a screen selected for phonetic similarity to the English language. Deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks with and without attention modules, were optimized with a focus on minimizing trainable parameters and utilizing small input window sizes for real-time application. These models were employed for discrete and continuous speech decoding tasks, achieving statistically significant participant-independent decoding performance for discrete classes and continuous characteristics of the produced audio signal. A frequency sub-band analysis highlighted the significance of certain frequency bands (delta, theta, and gamma) for decoding performance, and a perturbation analysis was used to identify crucial channels. Assessed channel selection methods did not significantly improve performance, suggesting a distributed representation of speech information encoded in the EEG signals. Leave-One-Out training demonstrated the feasibility of utilizing common speech neural correlates, reducing data collection requirements from individual participants.

美国有 700 多万人因神经系统疾病而影响了语言能力,对生活质量造成了不利影响。传统的语音界面,如眼球追踪 设备和 P300 拼写器,对这些患者来说既缓慢又不自然。另一种解决方案--语音脑机接口(BCI)可直接解码语音特征,提供更自然的交流机制。这项研究探索了利用无创脑电图解码语音特征的可行性。九名神经系统完好的参与者配备了 63 通道脑电图系统 ,并增加了传感器以消除眼部伪影。参与者朗读屏幕上显示的与英语语音相似的句子。深度学习模型包括卷积神经网络(Convolutional Neural Networks)和递归神经网络(Recurrent Neural Networks),有注意力模块和无注意力模块。这些模型被用于离散和连续语音解码任务,在离散类和连续特征音频信号的解码性能上取得了显著的与参与者无关的统计效果 。频率子带分析强调了某些频段(delta、theta和gamma)对解码性能的重要性,扰动分析用于识别关键信道。经过评估的信道选择方法并没有明显提高性能,这表明脑电信号中编码的语音信息是分布式的。留空训练证明了利用普通语音神经相关性的可行性,从而减少了对个别参与者的数据收集要求 。
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引用次数: 0
Identification of perceived sentences using deep neural networks in EEG. 利用脑电图中的深度神经网络识别感知句子。
Pub Date : 2024-10-30 DOI: 10.1088/1741-2552/ad88a3
Carlos Valle, Carolina Mendez-Orellana, Christian Herff, Maria Rodriguez-Fernandez

Objetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data.Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area.Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension.Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.

目标从大脑活动中解码语音可以帮助有语言障碍的人进行交流。深度神经网络在语音解码应用方面展现出巨大潜力。然而,包含语言障碍受试者神经记录的大型数据集的可用性有限,这构成了一项挑战。利用健康参与者的数据可以缓解这一限制,加快语音神经义肢的开发,同时最大限度地减少对特定患者训练数据的需求。在这项研究中,我们收集了大量数据集,包括 56 名健康参与者使用 64 个脑电图通道的记录。我们使用独立于主体、混合主体和微调方法对多个神经网络进行了训练,以对西班牙语中的感知句子进行分类。该数据集已公开发布,以促进该领域的进一步研究。我们的结果表明,在区分 30 个类别的句子身份方面,我们的准确性达到了很高的水平,这展示了利用脑电图训练深度神经网络(DNN)从感知语音中解码句子身份的可行性。值得注意的是,与受试者无关的方法与混合受试者方法的准确性相当,但受试者之间的差异更大。此外,我们的微调方法还获得了更高的准确率,这表明我们适应个别受试者特征的能力得到了提高,从而提高了性能。这表明,DNN 已经有效地学会了解码不同个体大脑活动的普遍特征,同时也能适应特定的参与者数据。此外,我们的分析表明,EEGNet 和 DeepConvNet 的性能相当,在句子身份解码方面优于 ShallowConvNet。最后,我们的 Grad-CAM 可视化分析确定了影响网络预测的关键区域,为语言感知和理解的神经过程提供了宝贵的见解。这些发现加深了我们对基于脑电图的语音感知解码的理解,为语音神经义肢的开发带来了希望,尤其是在受试者无法提供自己的训练数据的情况下。
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引用次数: 0
Feeling senseless sensations: a crossmodal EEG study of mismatched tactile and visual experiences in virtual reality. 无感的感觉:虚拟现实中不匹配的触觉和视觉体验的跨模态脑电图研究。
Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad83f5
Caroline Lehser, Steven A Hillyard, Daniel J Strauss

Objective.To create highly immersive experiences in virtual reality (VR) it is important to not only include the visual sense but also to involve multimodal sensory input. To achieve optimal results, the temporal and spatial synchronization of these multimodal inputs is critical. It is therefore necessary to find methods to objectively evaluate the synchronization of VR experiences with a continuous tracking of the user.Approach.In this study a passive touch experience was incorporated in a visual-tactile VR setup using VR glasses and tactile sensations in mid-air. Inconsistencies of multimodal perception were intentionally integrated into a discrimination task. The participants' electroencephalogram (EEG) was recorded to obtain neural correlates of visual-tactile mismatch situations.Main results.The results showed significant differences in the event-related potentials (ERP) between match and mismatch situations. A biphasic ERP configuration consisting of a positivity at 120 ms and a later negativity at 370 ms was observed following a visual-tactile mismatch.Significance.This late negativity could be related to the N400 that is associated with semantic incongruency. These results provide a promising approach towards the objective evaluation of visual-tactile synchronization in virtual experiences.

要在虚拟现实(VR)中创造高度沉浸式体验,重要的是不仅要包括视觉感官,还要涉及多模态感官 输入。要达到最佳效果,这些多模态输入的时空同步至关重要。因此,有必要找到客观评估 VR 体验与用户连续跟踪同步性的方法。本研究利用 VR 眼镜和半空中的触觉,在视觉-触觉 VR 设置中加入了被动触摸体验。多模态感知的不一致性被有意整合到了一项辨别任务中。研究人员记录了参与者的脑电图(EEG),以获得视觉-触觉不匹配情况下的神经相关性。结果显示,在匹配和不匹配情况下,事件相关电位(ERP)存在明显差异。在视觉-触觉错配后,观察到一种双相的ERP配置,包括120毫秒时的阳性和370毫秒时的阴性。这种晚期负性可能与语义不一致相关的N400有关。这些结果为客观评估虚拟体验中的视觉-触觉同步性提供了一种很有前景的方法 。
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引用次数: 0
Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding. 基于一维级联卷积自动编码器和自适应窗口阈值的癫痫发作自动检测。
Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad883a
Sunday Timothy Aboyeji, Xin Wang, Yan Chen, Ijaz Ahmad, Lin Li, Zhenzhen Liu, Chen Yao, Guoru Zhao, Yu Zhang, Guanglin Li, Shixiong Chen

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.Approach. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.Main results. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h-1respectively for a typical patient in CHB-MIT datasets.Significance. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

目的:在扩展脑电图记录中识别癫痫发作期(SOP)对于神经科医生有效诊断癫痫发作至关重要。然而,现有的许多用于癫痫发作检测(ESD)的计算机辅助诊断系统(CAD)主要侧重于区分脑电图记录中的发作期和发作间期状态。这一重点限制了它们在临床环境中的应用,因为这些系统通常依赖于需要标记数据的超级可视化学习方法:为解决这一问题,我们的研究采用一维级联卷积自动编码器(1D-CasCAE)为 ESD 引入了一种无监督学习框架。在这种方法中,首先将 CHB-MIT 数据集中选定患者的脑电图记录分割成 5 秒的历时。根据相关系数和香农熵选择八个信息通道。1D-CasCAE 的设计目的是通过下采样和上采样过程自主学习发作间期(非发作)片段的特征模式。自适应阈值和移动窗口的整合大大增强了模型的鲁棒性,使其能够在长时间的脑电图记录中准确识别发作节段:实验结果表明,所提出的 1D-CasCAE 能有效学习正常的脑电信号模式,并利用重建误差高效检测异常(发作节段)。与异常检测领域的其他主要方法相比,我们的模型表现出更优越的性能,其在 CHB-MIT 数据集中典型患者的平均平均值、灵敏度、特异性、预判和假阳性率分别为 98.00±3.51%、94.94±6.92%、99.60±0.30%、79.92±13.56% 和 0.0044±0.0030/h:最后,所开发的模型框架可用于临床环境,取代神经科医生对脑电图信号的人工检查过程。通过使用可变时间窗检测癫痫发作,该自动化系统可适应每位患者的 SOP。
{"title":"Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding.","authors":"Sunday Timothy Aboyeji, Xin Wang, Yan Chen, Ijaz Ahmad, Lin Li, Zhenzhen Liu, Chen Yao, Guoru Zhao, Yu Zhang, Guanglin Li, Shixiong Chen","doi":"10.1088/1741-2552/ad883a","DOIUrl":"10.1088/1741-2552/ad883a","url":null,"abstract":"<p><p><i>Objective</i>. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.<i>Approach</i>. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.<i>Main results</i>. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h<sup>-1</sup>respectively for a typical patient in CHB-MIT datasets.<i>Significance</i>. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Changes in high-frequency neural inputs to muscles during movement cancellation. 运动取消时肌肉高频神经输入的变化。
Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad8835
Blanka Zicher, Simon Avrillon, Jaime Ibáñez, Dario Farina

Objective.Cortical beta (13-30 Hz) and gamma (30-60 Hz) oscillations are prominent in the motor cortex and are known to be transmitted to the muscles despite their limited direct impact on force modulation. However, we currently lack fundamental knowledge about the saliency of these oscillations at spinal level. Here, we developed an experimental approach to examine the modulations in high-frequency inputs to motoneurons under different motor states while maintaining a stable force, thus constraining behaviour.Approach.Specifically, we acquired brain and muscle activity during a 'GO'/'NO-GO' task. In this experiment, the effector muscle for the task (tibialis anterior) was kept tonically active during the trials, while participants (N= 12) reacted to sequences of auditory stimuli by either keeping the contraction unaltered ('NO-GO' trials), or by quickly performing a ballistic contraction ('GO' trials). Motor unit (MU) firing activity was extracted from high-density surface and intramuscular electromyographic signals, and the changes in its spectral contents in the 'NO-GO' trials were analysed.Main results.We observed an increase in beta and low-gamma (30-45 Hz) activity after the 'NO-GO' cue in the MU population activity. These results were in line with the brain activity changes measured with electroencephalography. These increases in power occur without relevant alterations in force, as behaviour was restricted to a stable force contraction.Significance.We show that modulations in motor cortical beta and gamma rhythms are also present in muscles when subjects cancel a prepared ballistic action while holding a stable contraction in a 'GO'/'NO-GO' task. This occurs while force levels produced by the task effector muscle remain largely unaltered. Our results suggest that muscle recordings are informative also about motor states that are not force-control signals. This opens up new potential use cases of peripheral neural interfaces.

目的: 皮质β(13-30赫兹)和γ(30-60赫兹)振荡在运动皮质中非常突出,尽管它们对力量调节的直接影响有限,但已知它们会传递到肌肉。然而,我们目前对这些振荡在脊髓水平的显著性缺乏基本了解。在此,我们开发了一种实验方法来研究在不同运动状态下运动神经元的高频输入调节,同时保持稳定的力量,从而约束行为:具体来说,我们获取了 "GO"/"NO-GO "任务中的大脑和肌肉活动。在该实验中,任务的效应肌肉(胫骨前肌)在试验过程中保持强直性活动,而参与者(12 人)则对听觉刺激序列做出反应,要么保持收缩不改变("NO-GO "试验),要么快速进行弹道收缩("GO "试验)。我们从高密度表面和肌内肌电信号中提取了运动单元(MU)的发射活动,并分析了在 "NO-GO "试验中其频谱内容的变化。这些结果与脑电图测量到的大脑活动变化一致。由于行为仅限于稳定的力量收缩,因此这些力量的增加并没有引起力量的相关改变:我们的研究表明,当受试者在 "GO"/"NO-GO "任务中保持稳定收缩时取消准备好的弹道动作,肌肉中也会出现运动皮层 beta 和 gamma 节律的调节。这种情况发生时,任务效应肌肉产生的力水平基本保持不变。我们的研究结果表明,肌肉记录也能提供非力控制信号的运动状态信息。这为外周神经接口开辟了新的潜在用例。
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引用次数: 0
Neural decoding and feature selection methods for closed-loop control of avoidance behavior. 用于避让行为闭环控制的神经解码和特征选择方法。
Pub Date : 2024-10-29 DOI: 10.1088/1741-2552/ad8839
Jinhan Liu, Rebecca Younk, Lauren M Drahos, Sumedh S Nagrale, Shreya Yadav, Alik S Widge, Mahsa Shoaran

Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

目的:许多精神疾病都涉及过度回避或防御行为,例如焦虑症和创伤症中的回避或强迫症中的防御仪式。从局部场电位(LFP)中开发出预测这些行为的算法,可作为对此类疾病进行闭环控制的基础技术。一个重大挑战是确定编码这些防御行为的 LFP 特征。我们分析了接受音调冲击条件反射和消退的大鼠下边缘皮层和杏仁基底外侧的 LFP 信号,这是研究防御行为的标准。我们使用了一整套跨频谱、时间和连接域的神经标记物,并在光梯度提升机模型中使用 SHapley Additive exPlanations 进行特征重要性评估。我们的目标是解码三种常见的回避/防御行为:冻结、压杠抑制和运动(加速度测量),研究不同特征对解码性能的影响。频带功率和通道之间的频带功率比成为各次会议的最佳特征。高伽马(80-150 Hz)功率、功率比和区域间相关性比其他频段更有信息量,而其他频段与防御行为更有经典联系。专注于信息量大的特征可提高成绩。在对 16 名受试者进行的 4 次记录过程中,我们发现加速度测量挺举和杠铃按压率的平均决定系数分别为 0.5357 和 0.3476,皮尔逊相关系数分别为 0.7579 和 0.6092。仅利用信息量最大的特征就能发现加速度和压杆率之间的编码差异,前者主要通过局部频谱功率,后者则通过区域间连接。我们的方法显示了极低的训练/推理时间和内存使用率,只需要
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引用次数: 0
Robust entropy rate estimation for nonstationary neuronal calcium spike trains based on empirical probabilities. 基于经验概率的非稳态神经元钙离子尖峰列车稳健熵率估计
Pub Date : 2024-10-28 DOI: 10.1088/1741-2552/ad6cf4
Sathish Ande, Srinivas Avasarala, Sarpras Swain, Ajith Karunarathne, Lopamudra Giri, Soumya Jana

Objective. Temporal patterns in neuronal spiking encode stimulus uncertainty, and convey information about high-level functions such as memory and cognition. Estimating the associated information content and understanding how that evolves with time assume significance in the investigation of neuronal coding mechanisms and abnormal signaling. However, existing estimators of the entropy rate, a measure of information content, either ignore the inherent nonstationarity, or employ dictionary-based Lempel-Ziv (LZ) methods that converge too slowly for one to study temporal variations in sufficient detail. Against this backdrop, we seek estimates that handle nonstationarity, are fast converging, and hence allow meaningful temporal investigations.Approach. We proposed a homogeneous Markov model approximation of spike trains within windows of suitably chosen length and an entropy rate estimator based on empirical probabilities that converges quickly.Main results. We constructed mathematical families of nonstationary Markov processes with certain bi/multi-level properties (inspired by neuronal responses) with known entropy rates, and validated the proposed estimator against those. Further statistical validations were presented on data collected from hippocampal (and primary visual cortex) neuron populations in terms of single neuron behavior as well as population heterogeneity. Our estimator appears to be statistically more accurate and converges faster than existing LZ estimators, and hence well suited for temporal studies.Significance. The entropy rate analysis revealed not only informational and process memory heterogeneity among neurons, but distinct statistical patterns in neuronal populations (from two different brain regions) under basal and post-stimulus conditions. Taking inspiration, we envision future large-scale studies of different brain regions enabled by the proposed tool (estimator), potentially contributing to improved functional modeling of the brain and identification of statistical signatures of neurodegenerative diseases.

目的:神经元尖峰振荡的时间模式编码刺激的不确定性,并传递有关记忆和认知等高级功能的信息。估算相关的信息含量并了解其如何随时间演变,对于研究神经元编码机制和异常信号具有重要意义。然而,现有的熵率估算器(一种信息含量测量方法)要么忽略了固有的非平稳性,要么采用基于字典的 Lempel-Ziv (LZ) 方法,这种方法收敛速度太慢,无法对时间变化进行足够详细的研究。在此背景下,我们寻求能够处理非平稳性、快速收敛、从而进行有意义的时间研究的估算方法:我们提出了在适当长度的窗口内对尖峰列车进行同质马尔可夫模型近似的方法,以及基于经验概率的熵率估计器,该估计器收敛速度很快:我们构建了具有某些双/多级特性(受神经元反应的启发)的非平稳马尔可夫过程数学族,这些数学族具有已知的熵率,并针对这些数学族验证了所提出的估计器。从单个神经元行为和群体异质性的角度,对从海马(和初级视觉皮层)神经元群体收集的数据进行了进一步的统计验证。 我们的估计器在统计上似乎比现有的 LZ 估计器更准确,收敛速度更快,因此非常适合时间研究:熵率分析不仅揭示了神经元之间的信息和过程记忆异质性,还揭示了神经元群(来自两个不同脑区)在基础和刺激后条件下的不同统计模式。受此启发,我们设想未来将利用所提出的工具(估计器)对不同脑区进行大规模研究,从而为改进大脑功能建模和识别神经退行性疾病的统计特征做出潜在贡献。
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引用次数: 0
Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients. 研究双侧经颅磁刺激和虚拟现实脑机接口训练对慢性中风患者的协同神经调节作用
Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad8836
Monica Afonso, Francisco Sánchez-Cuesta, Yeray González-Zamorano, Juan Pablo Romero, Athanasios Vourvopoulos

Objective.Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.Approach.In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the event related desynchronization (ERD) and individual alpha frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.Main results.Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.Significance.This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.

目的:脑卒中是全球成年残疾人的主要致残原因,会导致运动障碍。为了恢复运动功能,患者需要接受康复训练,通常包括重复运动训练。对于缺乏自主运动能力的患者,新出现的基于技术的方法通过经颅磁刺激(TMS)等神经调节技术和脑机接口(BCIs)等闭环神经反馈技术,直接参与中枢神经系统。此外,虚拟现实技术(VR)多次提供的本体感觉反馈也可对此进行增强。然而,尽管越来越多的研究证明了每种技术的单独功效,但有关其综合效果的信息却十分有限:在这项研究中,我们分析了 10 名中风超过 4 个月的患者在接受重复 TMS 和 VR-BCI 训练的纵向干预期间获得的脑电图(EEG)信号。从脑电信号中提取了事件相关非同步化(ERD)和个体阿尔法频率(IAF),对其进行了长期评估,并将其与临床结果相关联:结果:治疗后,每位患者的临床疗效都有所改善。此外,IAF 值与临床结果有显著相关性,但干预前和干预后 ERD 的差异与临床改善之间没有关系:本研究为经颅磁刺激和虚拟现实脑干成像联合作用在促进患者康复方面的疗效提供了实证支持。该研究还表明,IAF 与康复效果之间存在关系,有可能成为中风康复的可检索生物标志物。
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引用次数: 0
Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications. 利用扩散张量成像建立脑组织电阻抗模型,用于功能神经外科应用。
Pub Date : 2024-10-24 DOI: 10.1088/1741-2552/ad7db2
Niranjan Kumar, Aidan Ahamparam, Charles W Lu, Karlo A Malaga, Parag G Patil

Objective.Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.Approach.We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.Main results.In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuchet al,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.Significance.This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locationsin vivo. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.

目的:几十年前,神经外科医生使用脑部电阻抗测量来进行粗略的术中组织分辨。随着时间的推移,这些技术在很大程度上被更精细的成像和电生理定位所取代。如今,先进的弥散张量成像(DTI)和有限元法(FEM)建模方法可实现无创、高分辨率的脑内阻抗预测。然而,目前还缺乏对人脑组织阻抗关系的预期和经过实验验证的阻抗建模参数。方法:我们使用有限元模拟高分辨率单电极和双电极阻抗测量,沿线性电极轨迹通过(1)典型灰质和白质组织模型,以及(2)基于全脑患者 DTI 模型的选定解剖结构。然后,我们将在已知位置沿脑深部刺激(DBS)手术轨迹进行的术中阻抗测量结果与模型预测结果进行比较,以评估模型的准确性并完善模型参数。虽然只有双电极配置对白质纤维方向敏感,但阻抗的其他影响因素,如白质密度,使得单电极阻抗测量即使在纯白质结构中也能显示出显著的空间变化。我们将五名 DBS 患者的 308 次术中单电极阻抗测量结果与一一对应位置的 DTI-FEM 预测结果进行了比较。根据这些数据校准模型系数后,所有患者的预测阻抗都能可靠地估计术中测量值(R=0.784±0.116,n=5)。通过这项研究,我们得出了 Tuch 等人发表的 DTI 传导模型斜率系数的最新值,即 k=0.0649S-s/mm3(原始 k=0.844),专门用于生理频率下的人体。意义:这是第一项将基于成像的人体脑组织模型的阻抗估计值与体内相同位置的实验测量值进行比较的研究。准确、无创、基于成像的阻抗预测在功能神经外科领域有很多应用,包括组织绘图、术中电极定位和 DBS。
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Journal of neural engineering
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