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Epilepsy is one of the most common neurological diseases, characterized bytransient and unprovoked events called epileptic seizures. Electroencephalogram(EEG) is an auxiliary method used to perform both the diagnosis and themonitoring of epilepsy. Given the unexpected nature of an epileptic seizure,its prediction would improve patient care, optimizing the quality of life andthe treatment of epilepsy. Predicting an epileptic seizure implies theidentification of two distinct states of EEG in a patient with epilepsy: thepreictal and the interictal. In this paper, we developed two deep learningmodels called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer(TMC-ViT), adaptations of Transformer-based architectures for multi-channeltemporal signals. Moreover, we accessed the impact of choosing differentpreictal duration, since its length is not a consensus among experts, and alsoevaluated how the sample size benefits each model. Our models are compared withfully connected, convolutional, and recurrent networks. The algorithms werepatient-specific trained and evaluated on raw EEG signals from the CHB-MITdatabase. Experimental results and statistical validation demonstrated that ourTMC-ViT model surpassed the CNN architecture, state-of-the-art in seizureprediction.

EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers