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The healthcare landscape is moving from the reactive interventions focused onsymptoms treatment to a more proactive prevention, from one-size-fits-all topersonalized medicine, and from centralized to distributed paradigms. WearableIoT devices and novel algorithms for continuous monitoring are essentialcomponents of this transition. Hyperdimensional (HD) computing is an emergingML paradigm inspired by neuroscience research with various aspects interestingfor IoT devices and biomedical applications. Here we explore the not yetaddressed topic of optimal encoding of spatio-temporal data, such aselectroencephalogram (EEG) signals, and all information it entails to the HDvectors. Further, we demonstrate how the HD computing framework can be used toperform feature selection by choosing an adequate encoding. To the best of ourknowledge, this is the first approach to performing feature selection using HDcomputing in the literature. As a result, we believe it can support the MLcommunity to further foster the research in multiple directions related tofeature and channel selection, as well as model interpretability.

Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection