Fig. 1

STFFDA model structure. The model consists of four main modules: the CNN module is used to extract the spatial features of EEG signals, the Bi-LSTM module captures the temporal dynamics of the signals, the 1D SE module highlights important features through channel weighting, and the attention module integrates and weights both spatial and temporal features to enhance the significance of key features. Finally, the fully connected layer classifies the epileptic signals. With this architecture, the STFFDA model effectively addresses the dependency on preprocessing in traditional methods and significantly improves classification accuracy.