  Recently Transformer and Convolution neural network (CNN) based models have
shown promising results in EEG signal processing. Transformer models can
capture the global dependencies in EEG signals through a self-attention
mechanism, 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 convolution
module into the Transformer encoder, EENED can learn the time-dependent
relationship of the patient's EEG signal features and notice local EEG abnormal
mutations closely related to epilepsy, such as the appearance of spikes and the
sprinkling of sharp and slow waves. Our proposed framework combines the ability
of Transformer and CNN to capture different scale features of EEG signals and
holds promise for improving the accuracy and reliability of epilepsy detection.
Our source code will be released soon on GitHub.
