Multi-view cross-subject seizure detection with information bottleneck attribution.

Significant progress has been witnessed in within-subject seizure detection from electroencephalography (EEG) signals. Consequently, more and more works have been shifted from within-subject seizure detection to cross-subject scenarios. However, the progress is hindered by inter-patient variations caused by gender, seizure type, etc.To tackle this problem, we propose a multi-view cross-object seizure detection model with information bottleneck attribution (IBA).Feature representations specific to seizures are learned from raw EEG data by adversarial deep learning. Combined with the manually designed discriminative features, the model can detect seizures across different subjects. In addition, we introduce IBA to provide insights into the decision-making of the adversarial learning process, thus enhancing the interpretability of the model.Extensive experiments are conducted on two benchmark datasets. The experimental results verify the efficacy of the model.