  Accurate classification of seizure types plays a crucial role in the
treatment and disease management of epileptic patients. Epileptic seizure types
not only impact the choice of drugs but also the range of activities a patient
can safely engage in. With recent advances being made towards artificial
intelligence enabled automatic seizure detection, the next frontier is the
automatic classification of seizure types. On that note, in this paper, we
explore the application of machine learning algorithms for multi-class seizure
type classification. We used the recently released TUH EEG seizure corpus
(V1.4.0 and V1.5.2) and conducted a thorough search space exploration to
evaluate the performance of a combination of various pre-processing techniques,
machine learning algorithms, and corresponding hyperparameters on this task. We
show that our algorithms can reach a weighted $F1$ score of up to 0.901 for
seizure-wise cross validation and 0.561 for patient-wise cross validation
thereby setting a benchmark for scalp EEG based multi-class seizure type
classification.
