Spatial neglect (SN) is a neurological disorder that causes inattention to visual stimuli in the contralesional visual field, stemming from unilateral brain injury such as stroke. The current gold standard method of SN assessment, the conventional Behavioral Inattention Test (BIT-C), is highly variable and inconsistent in its results. In our previous work, we built an augmented reality (AR)-based BCI to overcome the limitations of the BIT-C and classified between neglected and non-neglected targets with high accuracy. Our previous approach included personalization of the neglect detection classifier but the process required rigorous retraining from scratch and time-consuming feature selection for each participant. Future steps of our work will require rapid personalization of the neglect classifier; therefore, in this paper, we investigate fine-tuning of a neural network model to hasten the personalization process.