Cluster Embedding Joint-Probability-Discrepancy Transfer For Cross-subject Seizure Detection

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; p. 1
• Main Authors: Cui, Xiaonan, Cao, Jiuwen, Lai, Xiaoping, Jiang, Tiejia, Gao, Feng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Brain modeling; Centroids; Clustering; Computer applications; Convulsions & seizures; Correlation-alignment-based Source Selection; Data structures; Domain Adaptation; Domains; Electroencephalography; Embedding; Epilepsy; Feature extraction; Knowledge acquisition; Learning; Probability distribution; Prototypes; Regularization; Seizure Detection; Seizures; Testing; Transfer learning
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2022.3229066

Transfer learning (TL) has been applied in seizure detection to deal with differences between different subjects or tasks. In this paper, we consider cross-subject seizure detection that does not rely on patient history records, that is, acquiring knowledge from other subjects through TL to improve...