The rise of wearable EEG devices has opened the opportunity to develop new tools for neurological disorder monitoring, particularly for conditions like epilepsy. Machine learning plays a key role in processing the EEG signal towards an assessment of the person’s state, and eventually evaluating some condition risk. However, existing approaches often rely on raw EEG data, keeping a numerical representation of the information contained in the data. Conversely, in a previous work, we explored representing the EEG signals using sequential patterns. In this work, we analyze the potential of such a representation through several machine learning methods, including decision trees, support vector machines, k-nearest neighbors, and random forest. The experiments carried out with the CHB-MIT scalp EEG database of Physionet show the outperformance of random forest.