A scalable EEG-based spatial neglect detection system in augmented reality for stroke patients

Spatial neglect is a common visuospatial attention disorder following a stroke. To overcome weaknesses associated with classic pen-and-paper tests used in some clinical settings, we developed AREEN: an AR-guided EEG-based Neglect detection system. AREEN previously demonstrated that the EEG activity of patients with neglect was distinguishable from that of patients without neglect. However, to use this system practically, it would need to be able to diagnose neglect in new patients who have not been seen before, meaning the system should be able to generalize neglect detection.

In this study, we investigate the scalability of AREEN across individuals using multiple classification models. To determine the best classifier, four models (logistic regression, linear discriminant analysis, random forest, boosted tree) were tested and cross-validated with a leave-one-participant-out strategy.

The boosted tree model resulted in the highest average within-participant accuracies (proportion of EEG trials correctly classified) for both neglect, with a 76.0% average accuracy, and non-neglect, with a 68.2% average accuracy. It also yielded the highest within-group accuracies (proportion of patients within each group that correctly classified above 50% of EEG trials) for neglect 90.9% were correctly grouped, and for non-neglect 90.0%.

The application of this model would allow for accurate identification of spatial neglect, which could be crucial for determining stroke rehabilitation therapies. Patients also expressed high satisfaction, comfort, and willingness to continue using the system, based on responses to a questionnaire. Future developments of AREEN will aim to rehabilitate neglect by performing neglect detection in real-time with neurofeedback.