Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG DOI Creative Commons
Deniz Kocanaogullari, Richard Gall, Jennifer Y. Mak

и другие.

Journal of Neural Engineering, Год журнала: 2024, Номер 21(6), С. 066014 - 066014

Опубликована: Ноя. 5, 2024

Abstract Objective. We aim to assess the severity of spatial neglect (SN) through detailing patients’ field view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading inattention contralesional space. Commonly used Neglect detection methods like Behavioral Inattention Test—conventional lack capability full extent and neglect. Although Catherine Bergego Scale provides valuable clinical information, it does not detail specific FOV affected patients. Approach. Building on our previously developed EEG-based brain–computer interface system, AR-guided detection, assessment, rehabilitation system (AREEN), we map across patient’s FOV. have demonstrated that AREEN can patient-agnostic manner. However, its effectiveness patient-specific scenarios, which is crucial for creating generalizable plug-and-play remains unexplored. This paper introduces novel combined spatio-temporal network (ESTNet) processes both time frequency domain data capture essential band information associated with SN. also propose correction Bayesian fusion, leveraging AREEN’s recorded response times enhanced accuracy by addressing noisy labels within dataset. Main results. Extensive testing ESTNet proprietary dataset has superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, 86.36% specificity. Additionally, provide saliency maps enhance model explainability establish correlations. Significance. These findings underscore ESTNet’s potential fusion-based as an effective tool generalized assessment settings.

Язык: Английский

Application of Transfer Learning for Biomedical Signals: A Comprehensive Review of the Last Decade (2014-2024) DOI Creative Commons
Mahboobeh Jafari, Xiaohui Tao, Prabal Datta Barua

и другие.

Information Fusion, Год журнала: 2025, Номер 118, С. 102982 - 102982

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

0
A multidimensional adaptive transformer network for fatigue detection DOI
Dingming Wu, Liu Deng, Qiang Lü

и другие.

Cognitive Neurodynamics, Год журнала: 2025, Номер 19(1)

Опубликована: Фев. 20, 2025

Язык: Английский

Процитировано

0
A MultiModal Vigilance (MMV) dataset during RSVP and SSVEP brain-computer interface tasks DOI Creative Commons
Wei Wei, Kangning Wang, Shuang Qiu

и другие.

Scientific Data, Год журнала: 2024, Номер 11(1)

Опубликована: Авг. 10, 2024

Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability optimal performance of various tasks. In this report, we describe MultiModal (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) The BCI tasks encompass rapid serial visual presentation (RSVP)-based target image retrieval task steady-state evoked potential (SSVEP)-based cursor-control task. MMV includes four sessions for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), eye movement. provides data from stages: 1) raw data, 2) pre-processed 3) trial 4) feature that can be directly used vigilance estimation. We believe will achieve flexible reuse meet needs researchers. And greatly contribute advancing research on signal-based

Язык: Английский

Процитировано

2
A self-supervised graph network with time-varying functional connectivity for seizure prediction DOI
Boxuan Wei, Lu Xu, Jicong Zhang

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 102, С. 107375 - 107375

Опубликована: Дек. 24, 2024

Язык: Английский

Процитировано

1
Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG DOI Creative Commons
Deniz Kocanaogullari, Richard Gall, Jennifer Y. Mak

и другие.

Journal of Neural Engineering, Год журнала: 2024, Номер 21(6), С. 066014 - 066014

Опубликована: Ноя. 5, 2024

Abstract Objective. We aim to assess the severity of spatial neglect (SN) through detailing patients’ field view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading inattention contralesional space. Commonly used Neglect detection methods like Behavioral Inattention Test—conventional lack capability full extent and neglect. Although Catherine Bergego Scale provides valuable clinical information, it does not detail specific FOV affected patients. Approach. Building on our previously developed EEG-based brain–computer interface system, AR-guided detection, assessment, rehabilitation system (AREEN), we map across patient’s FOV. have demonstrated that AREEN can patient-agnostic manner. However, its effectiveness patient-specific scenarios, which is crucial for creating generalizable plug-and-play remains unexplored. This paper introduces novel combined spatio-temporal network (ESTNet) processes both time frequency domain data capture essential band information associated with SN. also propose correction Bayesian fusion, leveraging AREEN’s recorded response times enhanced accuracy by addressing noisy labels within dataset. Main results. Extensive testing ESTNet proprietary dataset has superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, 86.36% specificity. Additionally, provide saliency maps enhance model explainability establish correlations. Significance. These findings underscore ESTNet’s potential fusion-based as an effective tool generalized assessment settings.

Язык: Английский

Процитировано

0