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

Abstract

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