Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 831 - 839
• Main Authors: Peng, Ruimin, Du, Zhenbang, Zhao, Changming, Luo, Jingwei, Liu, Wenzhong, Chen, Xinxing, Wu, Dongrui
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain modeling; Classification; Coders; Deep learning; Distillation; EEG; Electric Power Supplies; Electroencephalography; Epilepsy; Feature extraction; Frequencies; Humans; knowledge distillation; Knowledge management; Machine Learning; seizure subtype classification; Seizures; Seizures - diagnosis; Training; Transformer; Transformers; Wavelet transforms

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may...