A key challenge that traditional ML methods suffer from is catastrophic forgetting, a phenomenon in which the model overwrites previously learned knowledge to learn new tasks. The field of continual learning aims to develop machine learning (ML) models capable of adapting to additional knowledge. Continual learning is essential to enable real-time epileptic seizure detection, which is currently a limiting factor, especially in a resource-constrained environment such as implantable edge devices. Biological metaplasticity has become a topic of interest as an application to these systems. This paper employs metaplasticity in a low-power binarized neural network (BNN) to maintain stable learning when EEG data is streamed into the network. Our model improves on baseline performances from traditional ML architectures. The metaplastic BNN in this paper reports performance metrics of accuracy and ROC-AUC values over 70%. Metaplasticity as a neuroscience-inspired continual learning approach holds promise as an epileptic seizure tracking strategy that is both patient-specific and adaptable to the variations that arise from real-world complexities. Note: This is a conference version of a journal article under review with new results.