Epileptic Seizure Detection Based on Convolutional Self-Attention Adaptive Dimensionality Expansion Network Using EEG Signals

Traditional epileptic seizure detection methods suffer from poor interpretability, excessive parameters, and fixed parameters in time-domain dimensionality expansion. To address these, this study proposes CSADI-Net, integrating convolutional self-attention and adaptive dimensionality expansion. Convolutional self-attention uses convolutional layers to generate Q (query, representing task-related attention cues), K (key, representing inherent signal characteristics), and V (value, representing input data), reducing trainable parameters. Adaptive dimensionality expansion combines with network training for parameter adjustment. Class activation heatmaps enable visual interpretability. Validated on CHB-MIT (Accuracy:98.87%, F1:98.49%) and TUH (Accuracy:98.26%, F1:98.13%) datasets, it outperforms CNN, CNN-LSTM, and linear self-attention Transformer. With high accuracy, anti-noise ability, and interpretability, it provides a new perspective for seizure detection.