  Epilepsy is one of the most common neurological disorders that can be
diagnosed through electroencephalogram (EEG), in which the following epileptic
events can be observed: pre-ictal, ictal, post-ictal, and interictal. In this
paper, we present a novel method for epilepsy detection into two
differentiation contexts: binary and multiclass classification. For feature
extraction, a total of 105 measures were extracted from power spectrum,
spectrogram, and bispectrogram. For classifier building, eight different
machine learning algorithms were used. Our method was applied in a widely used
EEG database. As a result, random forest and backpropagation based on
multilayer perceptron algorithms reached the highest accuracy for binary
(98.75%) and multiclass (96.25%) classification problems, respectively.
Subsequently, the statistical tests did not find a model that would achieve a
better performance than the other classifiers. In the evaluation based on
confusion matrices, it was also not possible to identify a classifier that
stands out in relation to other models for EEG classification. Even so, our
results are promising and competitive with the findings in the literature.
