EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently developed to address a related problem of recogniser robustness to uncontrollable signal variation which also occurs in automatic speech recognition (ASR). In this article we consider how some of the proved advantages of the "multi-stream combination" and "tandem" approaches in HMM/ANN hybrid based ASR can possibly be applied to improve the performance of EEG recognition.