  Clinical electroencephalographic (EEG) data varies significantly depending on
a number of operational conditions (e.g., the type and placement of electrodes,
the type of electrical grounding used). This investigation explores the
statistical differences present in two different referential montages: Linked
Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately
45% of the data in the TUH EEG Corpus. In this study, we explore the impact
this variability has on machine learning performance. We compare the
statistical properties of features generated using these two montages, and
explore the impact of performance on our standard Hidden Markov Model (HMM)
based classification system. We show that a system trained on LE data
significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We
also demonstrate that performance of a system trained on both data sets is
somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data
suggests that mean, variance and channel normalization should be considered.
However, cepstral mean subtraction failed to produce an improvement in
performance, suggesting that the impact of these statistical differences is
subtler.
