  Biological data like electroencephalography (EEG) are typically contaminated
by unwanted signals, called artifacts. Therefore, many applications dealing
with biological data with low signal-to-noise ratio require robust artifact
correction. For some applications like brain-computer-interfaces (BCI), the
artifact correction needs to be real-time capable. Artifact subspace
reconstruction (ASR) is a statistical method for artifact reduction in EEG.
However, in its current implementation, ASR cannot be used in mobile data
recordings using limited hardware easily. In this report, we add to the growing
field of portable, online signal processing methods by describing an
implementation of ASR for limited hardware like single-board computers. We
describe the architecture, the process of translating and compiling a Matlab
codebase for a research platform, and a set of validation tests using publicly
available data sets. The implementation of ASR on limited, portable hardware
facilitates the online interpretation of EEG signals acquired outside of the
laboratory environment.
