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Anchoring temporal convolutional networks for epileptic seizure prediction. 用于癫痫发作预测的锚定时序卷积网络
Pub Date : 2024-11-08 DOI: 10.1088/1741-2552/ad8bf3
Songhui Rao, Miaomiao Liu, Yin Huang, Hongye Yang, Jiarui Liang, Jiayu Lu, Yan Niu, Bin Wang

Objective. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to be shorter than the onset of antiepileptic, thus failing to prevent seizures. However, longer epilepsy prediction faces the problem that as the preictal period lengthens, it increasingly resembles the interictal period, complicating differentiation.Approach. To address these issues, we employ the sample entropy method for feature extraction from electroencephalography (EEG) signals. Subsequently, we introduce the anchoring temporal convolutional networks (ATCN) model for longer-term, patient-specific epilepsy prediction. ATCN utilizes dilated causal convolutional networks to learn time-dependent features from previous data, capturing temporal causal correlations within and between samples. Additionally, the model also incorporates anchoring data to enhance the performance of epilepsy prediction further. Finally, we proposed a multilayer sliding window prediction algorithm for seizure alarms.Main results. Evaluation on the Freiburg intracranial EEG dataset shows our approach achieves 100% sensitivity, a false prediction rate (FPR) of 0.09 per hour, and an average prediction time (APT) of 98.92 min. Using the CHB-MIT scalp EEG dataset, we achieve 97.44% sensitivity, a FPR of 0.12 per hour, and an APT of 93.54 min.Significance. These results demonstrate that our approach is adequate for seizure prediction over a more extended prediction range on intracranial and scalp EEG datasets. The APT of our approach exceeds the typical onset time of antiepileptic. This approach is particularly beneficial for patients who need to take medication at regular intervals, as they may only need to take their medication when our method issues an alarm. This capability has the potential to prevent seizures, which will greatly improve patients' quality of life.

目的:准确、及时地预测癫痫发作对患者减轻癫痫发作的影响或完全预防癫痫发作至关重要。目前的研究主要关注短期癫痫发作预测,这导致预测时间短于抗癫痫药物的起效时间,从而无法预防癫痫发作。然而,较长时间的癫痫预测面临的问题是,随着发作前时间的延长,它越来越像发作间期,从而使区分变得复杂:为解决这些问题,我们采用样本熵法从脑电图(EEG)信号中提取特征。随后,我们引入了锚定时序卷积网络(ATCN)模型,用于针对特定患者的长期癫痫预测。ATCN 利用扩张因果卷积网络从以前的数据中学习随时间变化的特征,捕捉样本内部和样本之间的时间因果相关性。此外,该模型还结合了锚定数据,以进一步提高癫痫预测的性能。最后,我们提出了一种用于癫痫发作警报的多层滑动窗口预测算法:在弗莱堡颅内脑电图数据集上进行的评估显示,我们的方法达到了 100% 的灵敏度,每小时错误预测率 (FPR) 为 0.08,平均预测时间 (APT) 为 99.98 分钟。使用 CHB-MIT 头皮脑电图数据集,我们的灵敏度达到 97.44%,误报率为每小时 0.11,平均预测时间为 92.99 分钟:这些结果表明,我们的方法足以在更大的预测范围内对颅内和头皮脑电图数据集进行癫痫发作预测。我们方法的平均预测时间超过了抗癫痫药物的典型起效时间。这种方法对需要定期服药的患者特别有利,因为只有当我们的方法发出警报时,他们才可能需要服药。这种能力有可能预防癫痫发作,从而大大提高患者的生活质量。
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引用次数: 0
SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands. SoftBoMI:用于将肩部运动映射到指令的非侵入式可穿戴体机接口。
Pub Date : 2024-11-08 DOI: 10.1088/1741-2552/ad8b6e
Rongkai Liu, Quanjun Song, Tingting Ma, Hongqing Pan, Hao Li, Xinyan Zhao

Objective.Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.Approach.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.Main results.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.Significance.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.

目的:用于控制辅助设备的定制化人机界面对于提高上肢截肢者和四肢瘫痪患者的自助能力至关重要。鉴于他们中的大多数人都拥有残余的肩部活动能力,利用它来生成操作辅助设备的指令可以作为脑机接口的一种补充方法:我们提出了一种混合型体机接口原型,它集成了软传感器和惯性测量单元。这项研究引入了基于规则的数据解码方法和基于用户意图推理的解码方法,将人体肩部动作映射为连续指令。此外,通过将用户操作性能的先验知识纳入共享自主框架,我们实施了一种自适应切换指令映射方法。这种方法实现了两种解码方法之间的无缝转换,增强了它们在不同任务中的适应性:通过一系列中心向外目标伸手任务和虚拟电动轮椅驾驶任务,在颈椎损伤者、双臂截肢者和健康人身上验证了所提出的方法。实验结果表明,使用软传感器和陀螺仪在意图推断方面表现最为全面。此外,基于规则的方法在轮椅操作方面表现出更好的动态性能,而意图推理方法更准确,但延迟更高。自适应切换解码方法通过在不同任务的解码方法之间无缝切换,提供了最佳的适应性。此外,我们还讨论了实验中各类参与者的差异和特点 意义:所提出的方法有望集成到服装中,实现与日常生活中的辅助设备的无创互动,并可作为未来康复评估的工具。
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引用次数: 0
Wide-angle simulated artificial vision enhances spatial navigation and object interaction in a naturalistic environment. 广角模拟人工视觉增强了自然环境中的空间导航和物体互动。
Pub Date : 2024-11-06 DOI: 10.1088/1741-2552/ad8b6f
Sandrine Hinrichs, Louise Placidet, Antonin Duret, Colas Authié, Angelo Arleo, Diego Ghezzi

Objective. Vision restoration approaches, such as prosthetics and optogenetics, provide visual perception to blind individuals in clinical settings. Yet their effectiveness in daily life remains a challenge. Stereotyped quantitative tests used in clinical trials often fail to translate into practical, everyday applications. On the one hand, assessing real-life benefits during clinical trials is complicated by environmental complexity, reproducibility issues, and safety concerns. On the other hand, predicting behavioral benefits of restorative therapies in naturalistic environments may be a crucial step before starting clinical trials to minimize patient discomfort and unmet expectations.Approach. To address this, we leverage advancements in virtual reality technology to conduct a fully immersive and ecologically valid task within a physical artificial street environment. As a case study, we assess the impact of the visual field size in simulated artificial vision for common outdoor tasks.Main results. We show that a wide visual angle (45°) enhances participants' ability to navigate and solve tasks more effectively, safely, and efficiently. Moreover, it promotes their learning and generalization capability. Concurrently, it changes the visual exploration behavior and facilitates a more accurate mental representation of the environment. Further increasing the visual angle beyond this value does not yield significant additional improvements in most metrics.Significance. We present a methodology combining augmented reality with a naturalistic environment, enabling participants to perceive the world as patients with retinal implants would and to interact physically with it. Combining augmented reality in naturalistic environments is a valuable framework for low vision and vision restoration research.

目的:义肢和光遗传学等视力恢复方法可在临床环境中为盲人提供视觉感知。然而,它们在日常生活中的有效性仍然是一个挑战。临床试验中使用的定型定量测试往往无法转化为实际的日常应用。一方面,由于环境的复杂性、可重复性问题和安全性问题,在临床试验中评估现实生活中的益处变得十分复杂。另一方面,在开始临床试验之前,预测自然环境中修复疗法的行为益处可能是一个关键步骤,可以最大限度地减少患者的不适感和未达到的预期。为了解决这个问题,我们利用虚拟现实技术的进步,在物理人工街道环境中开展了一项完全身临其境、生态有效的任务。作为一项案例研究,我们评估了模拟人工视觉中视野大小对常见户外任务的影响。我们的研究表明,广视角(45°)能提高参与者的导航能力,并能更有效、安全、高效地完成任务。此外,它还能提高他们的学习和概括能力。同时,它还能改变视觉探索行为,促进对环境更准确的心理表征。在大多数指标上,进一步增大视觉角度并不会带来显著的额外改善。我们提出了一种将增强现实与自然环境相结合的方法,使参与者能够像视网膜植入患者一样感知世界,并与之进行物理互动。将增强现实与自然环境相结合,是低视力和视力恢复研究的重要框架。
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引用次数: 0
Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI. 利用纹理闪烁:向实用、视觉舒适和高性能干式脑电图代码-VEP BCI 迈进。
Pub Date : 2024-11-05 DOI: 10.1088/1741-2552/ad8ef7
Frederic Dehais, Kalou Cabrera Castillos, Simon Ladouce, Pierre Clisson

Reactive Brain-Computer Interfaces (rBCIs) typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small, randomly-oriented Gabor or Ricker patches that optimize foveal neural response while reducing peripheral distraction. Methods: In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditional Plain flickers, Gabor-based, or Ricker-based flickers. These flickers were part of a five-class Code Visually Evoked Potentials (c-VEP) paradigm featuring low-frequency, short, and aperiodic visual flashes. Results: Subjective ratings revealed that Gabor and Ricker stimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover, Gabor and Ricker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 seconds of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings in naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 seconds. Conclusion: This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low-density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.

反应式脑机接口(rBCIs)通常依赖于重复的视觉刺激,这会给眼睛造成负担,并导致注意力分散。为了应对这些挑战,我们提出了一种植根于视觉神经科学的新方法,即设计增强反应视觉刺激(StAR)。StAR 刺激物由随机导向的小 Gabor 或 Ricker 补丁组成,可优化眼窝神经反应,同时减少周边分散:在一项因子设计研究中,24 名配备了 8 个干电极脑电图系统的参与者将注意力集中在以三种形式呈现的一系列目标闪烁上:传统的 Plain 闪烁、基于 Gabor 或基于 Ricker 的闪烁。这些闪烁是五级编码视觉诱发电位(c-VEP)范式的一部分,具有低频、短时和非周期性视觉闪烁的特点:主观评价显示,与普通闪烁相比,Gabor 和 Ricker 刺激视觉舒适,在周边视觉中几乎不可见。此外,与普通闪烁纹理(65.6%)相比,基于 Gabor 和 Ricker 的纹理只需 88 秒的校准数据就能达到更高的准确率(分别为 93.6% 和 96.3%)。为了在自然操作中验证我们的研究结果,我们对该实验进行了后续的在线实施。在这次试验中,提示任务的准确率达到了 97.5%,异步数字编码任务的准确率达到了 94.3%,平均解码时间低至 1.68 秒:这项研究表明,通过将视觉上不显眼的系统与无凝胶、低密度脑电图技术相结合,有可能将 BCI 应用扩展到实验室以外的领域,从而使 BCI 更方便、更高效。数据集、算法和 BCI 实现可通过开放访问存储库共享。
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引用次数: 0
Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG. 利用脑电图对中风患者的视觉疏忽严重程度进行估计。
Pub Date : 2024-11-05 DOI: 10.1088/1741-2552/ad8efc
Deniz Kocanaogullari, Richard Gall, Jennifer Mak, Xiaofei Huang, Katie Mullen, Sarah Ostadabbas, George Wittenberg, Emily Stafford Grattan, Murat Akcakaya

Objective: We aim to assess the severity of spatial neglect through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test - Conventional (BIT-C) lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale (CBS) provides valuable clinical information, it does not detail the specific field of view affected in neglect patients.

Approach: Building on our previously developed EEG-based Brain-Computer Interface (BCI) system, AREEN (AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System), we aim to map neglect severity across a patient's field of view. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined Spatio-Temporal Network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with spatial neglect. We also propose a field of view correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.

Main results: Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.

Significance: These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical setting.

研究目的我们的目的是通过脑电图详细显示患者的视野(FOV)来评估空间忽略的严重程度。空间忽略是中风患者普遍存在的一种神经综合征,通常由单侧脑损伤引起,导致患者对对侧空间注意力不集中。常用的疏忽检测方法,如常规行为性注意力缺失测试(BIT-C),无法全面评估疏忽的范围和严重程度。虽然凯瑟琳-伯格戈量表(CBS)提供了有价值的临床信息,但它并没有详细说明忽视患者受影响的特定视野:基于我们之前开发的基于脑电图的脑机接口(BCI)系统 AREEN(AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System,基于脑电图的忽视检测、评估和康复系统),我们的目标是绘制患者整个视野的忽视严重程度图。我们已经证明,AREEN 能够以一种与患者无关的方式评估忽视的严重程度。然而,它在特定患者场景中的有效性仍有待探索,而这对于创建一个可通用的即插即用系统至关重要。本文介绍了一种新颖的基于脑电图的组合时空网络(ESTNet),它能处理时域和频域数据,捕捉与空间忽略相关的重要频段信息。我们还提出了一种使用贝叶斯融合的视场校正系统,利用 AREEN 记录的响应时间,通过处理数据集中的噪声标签来提高准确性:在我们的专有数据集上对ESTNet进行的广泛测试表明,ESTNet优于基准方法,准确率达到79.62%,灵敏度达到76.71%,特异性达到86.36%。此外,我们还提供了突出图,以增强模型的可解释性并建立临床相关性:这些研究结果凸显了ESTNet与基于贝叶斯融合的FOV校正相结合的潜力,是在临床环境中进行广义忽视评估的有效工具。
{"title":"Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.","authors":"Deniz Kocanaogullari, Richard Gall, Jennifer Mak, Xiaofei Huang, Katie Mullen, Sarah Ostadabbas, George Wittenberg, Emily Stafford Grattan, Murat Akcakaya","doi":"10.1088/1741-2552/ad8efc","DOIUrl":"https://doi.org/10.1088/1741-2552/ad8efc","url":null,"abstract":"<p><strong>Objective: </strong>We aim to assess the severity of spatial neglect through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test - Conventional (BIT-C) lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale (CBS) provides valuable clinical information, it does not detail the specific field of view affected in neglect patients.</p><p><strong>Approach: </strong>Building on our previously developed EEG-based Brain-Computer Interface (BCI) system, AREEN (AR-guided EEG-based Neglect Detection, Assessment, and Rehabilitation System), we aim to map neglect severity across a patient's field of view. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined Spatio-Temporal Network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with spatial neglect. We also propose a field of view correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.</p><p><strong>Main results: </strong>Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.</p><p><strong>Significance: </strong>These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical setting.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584031","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
High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets. 利用导电墨水和聚合物厚膜网实现高质量多模态磁共振成像与同步脑电图。
Pub Date : 2024-11-05 DOI: 10.1088/1741-2552/ad8837
Nicholas G Cicero, Nina E Fultz, Hongbae Jeong, Stephanie D Williams, Daniel Gomez, Beverly Setzer, Tracy Warbrick, Manfred Jaschke, Ravij Gupta, Michael Lev, Giorgio Bonmassar, Laura D Lewis

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets.Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality.Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.

目标 结合磁共振成像(MRI)和脑电图(EEG)为研究不同空间和时间尺度的大脑功能提供了强有力的工具。同时采集两种模式可提供单一模式无法显示的独特信息。然而,由于市售磁共振条件脑电图网的图像质量和安全性限制,目前的脑电图-磁共振成像同步研究仅限于一小部分磁共振成像序列。我们测试了基于高阻聚合物厚膜 (PTF) 的脑电图网 Inknet2(使用导电墨水)能否通过减少传统磁共振条件网造成的射频屏蔽,以最小的伪影获取各种磁共振图像模式。然后,我们进行了幻影扫描,以测试使用传统铜脑电图网、新型 Inknet2 和不使用任何脑电图网时的图像质量。最后,我们在 3 Tesla (3T) 和 7 Tesla (7T) 下分别扫描了五名人体受试者和三名人体受试者,分别使用和不使用 Inknet2 来评估结构性和功能性 MRI 图像质量。 主要结果 在这些模拟、模型扫描和人体研究中,Inknet2 比传统磁网引起的伪影更少,产生的图像质量与不使用任何磁网的扫描相似。 意义 我们的研究结果表明,使用导电墨水和聚合物厚膜技术制造的脑电图网可以在 3T 和 7T 下通过各种磁共振成像脉冲序列实现高质量的结构和功能多模态成像。
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引用次数: 0
Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance. 面向任务的脑电图去噪生成对抗网络,用于提高 SSVEP-BCI 性能。
Pub Date : 2024-11-05 DOI: 10.1088/1741-2552/ad8963
Pu Zeng, Liangwei Fan, You Luo, Hui Shen, Dewen Hu

Objective.The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.Approach.To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.Main results.We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.Significance.This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.

目的: 脑电图(EEG)信号的质量直接影响脑机接口(BCI)任务的性能。人们提出了许多方法来消除脑电信号中的噪声,但这些方法大多只关注信号去噪本身,而忽略了对后续任务的影响,这偏离了脑电去噪的初衷。本研究的主要目的是优化脑电图去噪模型,以提高 BCI 任务的性能。 为此,我们提出了一种创新的任务导向脑电图去噪生成对抗网络(TOED-GAN)方法。该网络利用 GAN 的生成器从原始脑电信号中分解和重建干净信号,并利用鉴别器学习如何将生成的信号与真正的干净信号区分开来,从而通过同时增强任务相关成分和去除原始污染信号中与任务无关的噪声,显著提高信噪比 (SNR)。 主要结果 我们分别在一个公共数据集和一个自选数据集上评估了该模型的性能,并对基于稳态视觉诱发电位(SSVEP)的BCI进行了典型相关分析(CCA)分类任务。实验结果表明,TOED-GAN 在去除脑电噪声和提高 SSVEP-BCI 性能方面表现出色,与卷积神经网络的基线方法相比,准确率分别提高了 18.47% 和 21.33% 意义 这项工作证明,所提出的 TOED-GAN 作为一种为 SSVEP 任务定制的脑电去噪方法,有助于提高 BCI 在实际应用场景中的性能。
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引用次数: 0
Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features. 使用脑电图源空间功能连接特征进行聚类,以减少驾驶疲劳分类中的受试者变异性。
Pub Date : 2024-11-05 DOI: 10.1088/1741-2552/ad8b6d
Khanh Ha Nguyen, Yvonne Tran, Ashley Craig, Hung Nguyen, Rifai Chai

Objective.While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings.Approach.In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.Significance.Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.

虽然基于脑电图(EEG)的驾驶员疲劳状态分类模型已证明有效,但其在现实世界中的应用仍不确定。不同个体之间的脑电信号存在很大差异,这对开发通用模型构成了挑战,往往需要在引入新受试者后进行重新训练。然而,在现实世界中,获取足够的数据进行再训练,尤其是新受试者的疲劳数据是不切实际的。为了应对这些挑战,本文介绍了一种将聚类与分类相结合的疲劳检测混合解决方案。无监督聚类根据受试者在警戒状态下的脑电图功能连接性对其进行分组,随后将分类模型应用于每个聚类,以预测警戒和疲劳状态。结果表明,与没有聚类的情况相比,聚类分类的准确率更高,这表明通过聚类成功地将具有相似功能连接特性的受试者分组,从而增强了分类过程。此外,所提出的混合方法确保了实际可行的再训练过程,提高了疲劳检测系统在实际应用中的适应性和有效性。
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引用次数: 0
Safety of non-invasive brain stimulation in patients with implants: a computational risk assessment. 对植入物患者进行非侵入性脑部刺激的安全性:计算风险评估。
Pub Date : 2024-11-05 DOI: 10.1088/1741-2552/ad8efa
Fariba Karimi, Antonino M Cassarà, Myles Capstick, Niels Kuster, Esra Neufeld

Objective: Non-invasive brain stimulation (NIBS) methodologies, such as transcranial electric (tES) are increasingly employed for therapeutic, diagnostic, or research purposes. The concurrent presence of active/passive implants can pose safety risks, affect the NIBS delivery, or generate confounding signals. A systematic investigation is required to understand the interaction mechanisms, quantify exposure, assess risks, and establish guidance for NIBS applications.

Approach: We used measurements, simplified generic, and detailed anatomical modeling to: (i) systematically analyze exposure conditions with passive and active implants, considering local field enhancement, exposure dosimetry, tissue heating and neuromodulation, capacitive lead current injection, low-impedance pathways between electrode contacts, and insulation damage; (ii) identify risk metrics and efficient prediction strategies; (iii) quantify these metrics in relevant exposure cases and (iv) identify worst case conditions. Various aspects including implant design, positioning, scar tissue formation, anisotropy, and frequency were investigated.

Results: At typical tES frequencies, local enhancement of dosimetric exposure quantities can reach up to one order of magnitude for deep brain stimulation (DBS) and stereoelectroencephalography implants (more for elongated passive implants), potentially resulting in unwanted neuromodulation that can confound results but is still 2-3 orders of magnitude lower than active DBS. Under worst-case conditions, capacitive current injection in the active implants' lead can produce local exposures of similar magnitude as the passive field enhancement, while capacitive pathways between contacts are negligible. Above 10 kHz, applied current magnitudes increase, necessitating consideration of tissue heating. Furthermore, capacitive effects become more prominent, leading to current injection that can reach DBS-like levels. Adverse effects from abandoned/damaged leads in direct electrode vicinity cannot be excluded.

Significance: Safety related concerns of tES application in the presence of implants are systematically identified and explored, resulting in specific and quantitative guidance and establishing basis for safety standards. Furthermore,several methods for reducing risks are suggested while acknowledging the limitations(see Sec. 4.5).

目的:经颅电刺激(tES)等非侵入性脑刺激(NIBS)方法越来越多地被用于治疗、诊断或研究目的。有源/无源植入物的同时存在会带来安全风险,影响 NIBS 的传输,或产生干扰信号。需要进行系统调查,以了解相互作用机制、量化暴露、评估风险并为 NIBS 应用制定指导原则:方法:我们利用测量、简化通用模型和详细解剖模型来方法:我们使用测量结果、简化通用方法和详细的解剖模型:(i) 系统分析被动和主动植入物的暴露条件,考虑局部场增强、暴露剂量测定、组织加热和神经调制、电容性导联电流注入、电极触点之间的低阻抗通路和绝缘损坏;(ii) 确定风险指标和有效的预测策略;(iii) 量化相关暴露案例中的这些指标;(iv) 确定最坏情况。研究了包括植入物设计、定位、瘢痕组织形成、各向异性和频率在内的各个方面:结果:在典型的 tES 频率下,脑深部刺激(DBS)和立体脑电图植入体的剂量学暴露量的局部增强可达一个数量级(加长型被动植入体的增强更大),可能导致不必要的神经调节,从而混淆结果,但仍比有源 DBS 低 2-3 个数量级。在最坏的情况下,有源植入体导线中的电容电流注入会产生与被动场增强类似程度的局部暴露,而触点之间的电容通路可以忽略不计。当频率超过 10 kHz 时,外加电流幅度会增大,这就需要考虑组织发热问题。此外,电容效应会变得更加突出,导致电流注入达到类似 DBS 的水平。不排除直接电极附近的废弃/损坏导线会产生不良影响:意义:系统地确定和探讨了在植入物存在的情况下应用 tES 所涉及的安全问题,从而提供了具体的量化指导,并为安全标准奠定了基础。此外,在承认局限性的同时,还提出了几种降低风险的方法(见第 4.5 节)。
{"title":"Safety of non-invasive brain stimulation in patients with implants: a computational risk assessment.","authors":"Fariba Karimi, Antonino M Cassarà, Myles Capstick, Niels Kuster, Esra Neufeld","doi":"10.1088/1741-2552/ad8efa","DOIUrl":"https://doi.org/10.1088/1741-2552/ad8efa","url":null,"abstract":"<p><strong>Objective: </strong>Non-invasive brain stimulation (NIBS) methodologies, such as transcranial electric (tES) are increasingly employed for therapeutic, diagnostic, or research purposes. The concurrent presence of active/passive implants can pose safety risks, affect the NIBS delivery, or generate confounding signals. A systematic investigation is required to understand the interaction mechanisms, quantify exposure, assess risks, and establish guidance for NIBS applications.</p><p><strong>Approach: </strong>We used measurements, simplified generic, and detailed anatomical modeling to: (i) systematically analyze exposure conditions with passive and active implants, considering local field enhancement, exposure dosimetry, tissue heating and neuromodulation, capacitive lead current injection, low-impedance pathways between electrode contacts, and insulation damage; (ii) identify risk metrics and efficient prediction strategies; (iii) quantify these metrics in relevant exposure cases and (iv) identify worst case conditions. Various aspects including implant design, positioning, scar tissue formation, anisotropy, and frequency were investigated.</p><p><strong>Results: </strong>At typical tES frequencies, local enhancement of dosimetric exposure quantities can reach up to one order of magnitude for deep brain stimulation (DBS) and stereoelectroencephalography implants (more for elongated passive implants), potentially resulting in unwanted neuromodulation that can confound results but is still 2-3 orders of magnitude lower than active DBS. Under worst-case conditions, capacitive current injection in the active implants' lead can produce local exposures of similar magnitude as the passive field enhancement, while capacitive pathways between contacts are negligible. Above 10 kHz, applied current magnitudes increase, necessitating consideration of tissue heating. Furthermore, capacitive effects become more prominent, leading to current injection that can reach DBS-like levels. Adverse effects from abandoned/damaged leads in direct electrode vicinity cannot be excluded.</p><p><strong>Significance: </strong>Safety related concerns of tES application in the presence of implants are systematically identified and explored, resulting in specific and quantitative guidance and establishing basis for safety standards. Furthermore,several methods for reducing risks are suggested while acknowledging the limitations(see Sec. 4.5).</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584010","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
Estimating cognitive workload using a commercial in-ear EEG headset. 使用商用耳内脑电图耳机估算认知工作量。
Pub Date : 2024-11-05 DOI: 10.1088/1741-2552/ad8ef8
Christoph Tremmel, Dean J Krusienski, M C Schraefel

Objective: This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN "Guardian". Approach: Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance of gamma band activity in workload estimations. Therefore, each signal was analyzed across low frequency (1-35 Hz) and high frequency (1-100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined. Main results: Workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequency gamma band features can improve workload estimation. Significance: The application of EEG-based Brain-Computer Interfaces (BCIs) beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.

研究目的本研究使用商用耳内脑电图(EEG)系统 IDUN "Guardian",对在两项不同任务中估计各种脑力劳动负荷水平的潜力进行了调查:受试者完成了两项经典的脑力劳动任务:n-back 任务和心算任务。在完成这些任务的过程中,同时收集耳内和传统脑电图数据。为了便于进行更全面的比较,我们有意提高了任务的复杂性,使其超出了一般水平。我们还特别强调要了解伽玛波段活动在工作量估算中的重要性。因此,对每个信号都进行了低频(1-35 赫兹)和高频(1-100 赫兹)范围的分析。此外,还从常规脑电图记录中提取并检查了替代耳内脑电图测量值:使用耳内脑电图估算工作量的结果具有显著的统计学意义,在 n-back 任务中,四个等级的工作量估算率为 44.1%,两个等级的工作量估算率为 68.4%,超过了偶然水平。与耳内脑电图相比,传统脑电图的性能明显更高,在相应任务中分别达到了 80.3% 和 92.9% 的准确率,错误率也低于天真预测器。所开发的替代测量方法取得了更好的结果,准确率分别达到 57.5% 和 85.5%,从而为增强未来的耳内式系统提供了启示。值得注意的是,大多数高频范围的信号在准确性方面优于低频范围的信号,这验证了高频伽玛频段特征可以改善工作量估算:基于脑电图的脑机接口(BCI)在实验室以外的应用往往受到实际限制的阻碍。入耳式脑电图系统为这一问题提供了一个很有前景的解决方案,有可能实现日常使用。本研究评估了商用入耳式耳机的性能,并提供了提高效率的指导原则。
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引用次数: 0
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Journal of neural engineering
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