The processing of electroencephalograms (EEGs) is a growing field where mature speech processing techniques are able to rapidly progress development and understanding of the associated neuroscience. I-vectors and Joint Factor Analysis (JFA), along with their foundational universal background models (UBMs) have progressed to a level of understanding that makes them prime for transition to the EEG community. To prove the capability of these techniques they are tested against two contrasting EEG data sets, PhysioNet's EEG Motor Movement/Imagery Dataset and the Temple University Hospital EEG Corpus, to highlight the effectiveness of the techniques with minimal domain knowledge modifications. The initial results, presented as equal error rates as low as 20%, support the development of these techniques as a viable approach to addressing subject verification within and across subjects.