EEG-based seizure prediction via Transformer guided CNN

Abstract

Recently, most seizure prediction methods mainly utilize pure CNN or Transformer model, which cannot extract local and global features simultaneously. To this end, we propose an Electroencephalogram (EEG) seizure prediction method based on Transformer guided CNN (TGCNN), which combines the complementary advantages of CNN and Transformer. The proposed method first use short-time Fourier transform (STFT) to extract time–frequency features from EEG signals. Then, these features are fed into the alternating structure to model both local feature and long-distance dependencies, which can overcome both the deficiency of long distance dependence in CNN and the lack of local features in Transformer. Finally, the prediction result is obtained through a global average pooling layer and fully connected layer. The proposed method achieves sensitivity of 91.5%, false prediction rate (FPR) of 0.145/h, and area under curve (AUC) of 93.5% on CHB-MIT database and 82.2% sensitivity, 0.06/h FPR, and 83.5% AUC on Kaggle dataset.