The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) provides a simplified learning framework for cognitive tasks. Spatio-temporal (ST) signal processing, which encompasses bio-signals such as electromyography (EMG) and electroencephalography (EEG), is one family of applications that benefits from an HDC-based framework. HDC is inherently ill-suited to conventional computing platforms based on the von-Neumann architecture. In this work, we propose an architecture for ST signal processing within the HDC framework using predominantly in-memory compute arrays. In particular, we introduce a methodology for in-memory hyperdimensional encoding of ST data to be used together with an in-memory associative search module.