Patient monitoring require the acquisition of always larger amounts of biosignal data that needs to be managed and transferred with minimum energy consumption. In order to reduce data dimensionality, subspace analysis can be considered a fundamental tool that can be used to highly reduce the size of high-dimensional data, thus minimizing the requirements for data transfer. In order to use subspace analysis methods with the minimum requirements in terms of cost and energy consumption, we propose here two specialized streaming algorithms for the estimation of the subspace of electroencephalogram (EEG) signals directly after the acquisition on edge devices. The implementation of the algorithms is tested on a common low-end microcontroller unit (MCU), which is an ideal candidate as edge computing digital hardware platform. The functional performance of these methods is evaluated along with the requirements in term of computational time, energy consumption and memory footprint.