Wearable IoT devices and novel continuous monitoring algorithms are essential components of the healthcare transition from reactive interventions focused on symptom treatment to more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized to distributed paradigms. HyperDimensional Computing (HDC) is an emerging ML paradigm inspired by neuroscience research with various aspects interesting for IoT devices and biomedical applications. In this work, we explore five HD vector encoding strategies of spatio-temporal ExG data, such as that of electroencephalogram (EEG), and test it on a use case of epileptic seizure detection. We discuss the impact of these strategies' performance, memory overhead, and computational complexity. Furthermore, we demonstrate how feature selection via the HDC framework can be accomplished by choosing a proper encoding, and results in up to 70% reduction in used features while improving performance up to 7%.