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The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent and requires experienced specialists to annotate seizure events. It is clinically time-consuming and error-prone to identify seizure activities by visually scanning EEG signals. Since EEG data are heavily under-represented, supervised learning techniques are not always practical, particularly when the data is not sufficiently labelled. Visualization of EEG data in low-dimensional feature space can ease the annotation to support subsequent supervised learning for seizure detection. Here, we leverage the benefit of both the time-frequency domain features and the Deep Boltzmann Machine (DBM) based unsupervised learning techniques to represent EEG signals in a 2-dimensional (2D) feature space. A novel unsupervised learning approach based on DBM, namely DBM_transient, is proposed by training DBM to a transient …