Mutual Information Driven Representation Learning for Cross-Subject Seizure Detection

• Published in: IEEE signal processing letters Vol. 32; pp. 2434 - 2438
• Main Authors: Hu, Dinghan, Cui, Xiaonan, Jiang, Tiejia, Hu, Wenbin, Cao, Jiuwen
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Brain modeling; Cognitive tasks; Convulsions & seizures; Decomposition; Electroencephalography; Epilepsy; Feature extraction; individual variability; Machine learning; Mutual information; Representation learning; Representations; Seizure detection; Spatiotemporal phenomena; Temperature measurement; Training; transfer learning

Developing a generalizable model across subjects is crucial for the practical application of Electroencephalogram (EEG) based seizure detection model. However, inter-subject variability poses a challenge to the accurate identification of epileptic EEG, and applications often require recalibration an...