Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.

Deniz Kocanaogullari, Richard Gall, Jennifer Mak, Xiaofei Huang, Katie Mullen, Sarah Ostadabbas, George F Wittenberg, Emily S Grattan, Murat Akcakaya
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Abstract

Objective.We aim to assess the severity of spatial neglect (SN) 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 lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale provides valuable clinical information, it does not detail the specific FOV affected in neglect patients.Approach.Building on our previously developed EEG-based brain-computer interface system, AR-guided EEG-based neglect detection, assessment, and rehabilitation system (AREEN), we aim to map neglect severity across a patient's FOV. 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 SN. We also propose a FOV 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 settings.

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利用脑电图对中风患者的视觉疏忽严重程度进行估计。
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。
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Temporal attention fusion network with custom loss function for EEG-fNIRS classification. Classification of hand movements from EEG using a FusionNet based LSTM network. Frequency-dependent phase entrainment of cortical cell types during tACS: computational modeling evidence. Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG. SSVEP modulation via non-volitional neurofeedback: An in silico proof of concept.
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