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.

翻译：近年来，基于Transformer和卷积神经网络（CNN）的模型在脑电图（EEG）信号处理中展现出良好效果。Transformer模型通过自注意力机制可捕捉EEG信号中的全局依赖关系，而CNN模型能捕获锯齿波等局部特征。本研究提出一种端到端神经癫痫检测模型EENED，该模型融合了CNN与Transformer。具体而言，通过在Transformer编码器中引入卷积模块，EENED能够学习患者EEG信号特征的时域依赖关系，并关注与癫痫密切相关的局部EEG异常突变（如棘波出现及尖慢波发散）。我们所提出的框架融合了Transformer与CNN对EEG信号多尺度特征的捕获能力，有望提升癫痫检测的准确性与可靠性。相关源代码即将在GitHub上发布。