Spatial neglect (SN) is a neurological syndrome in stroke patients, commonly due to unilateral brain injury. It results in inattention to stimuli in the contralesional visual field. The current gold standard for SN assessment is the behavioral inattention test (BIT). BIT includes a series of penand-paper tests. These tests can be unreliable due to high variablility in subtest performances; they are limited in their ability to measure the extent of neglect, and they do not assess the patients in a realistic and dynamic environment. In this paper, we present an electroencephalography (EEG)-based brain-computer interface (BCI) that utilizes the Starry Night Test to overcome the limitations of the traditional SN assessment tests. Our overall goal with the implementation of this EEG-based Starry Night neglect detection system is to provide a more detailed assessment of SN. Specifically, to detect the presence of SN and its severity. To achieve this goal, as an initial step, we utilize a convolutional neural network (CNN) based model to analyze EEG data and accordingly propose a neglect detection method to distinguish between stroke patients without neglect and stroke patients with neglect.Clinical relevance-The proposed EEG-based BCI can be used to detect neglect in stroke patients with high accuracy, specificity and sensitivity. Further research will additionally allow for an estimation of a patient's field of view (FOV) for more detailed assessment of neglect.