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Recently Transformer and Convolution neural network (CNN) based models haveshown promising results in EEG signal processing. Transformer models cancapture the global dependencies in EEG signals through a self-attentionmechanism, while CNN models can capture local features such as sawtooth waves.In this work, we propose an end-to-end neural epilepsy detection model, EENED,that combines CNN and Transformer. Specifically, by introducing the convolutionmodule into the Transformer encoder, EENED can learn the time-dependentrelationship of the patient's EEG signal features and notice local EEG abnormalmutations closely related to epilepsy, such as the appearance of spikes and thesprinkling of sharp and slow waves. Our proposed framework combines the abilityof Transformer and CNN to capture different scale features of EEG signals andholds promise for improving the accuracy and reliability of epilepsy detection.Our source code will be released soon on GitHub.

EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer