  Feature extraction for automatic classification of EEG signals typically
relies on time frequency representations of the signal. Techniques such as
cepstral-based filter banks or wavelets are popular analysis techniques in many
signal processing applications including EEG classification. In this paper, we
present a comparison of a variety of approaches to estimating and
postprocessing features. To further aid in discrimination of periodic signals
from aperiodic signals, we add a differential energy term. We evaluate our
approaches on the TUH EEG Corpus, which is the largest publicly available EEG
corpus and an exceedingly challenging task due to the clinical nature of the
data. We demonstrate that a variant of a standard filter bank-based approach,
coupled with first and second derivatives, provides a substantial reduction in
the overall error rate. The combination of differential energy and derivatives
produces a 24% absolute reduction in the error rate and improves our ability to
discriminate between signal events and background noise. This relatively simple
approach proves to be comparable to other popular feature extraction approaches
such as wavelets, but is much more computationally efficient.
