MCDDT: Mirror Center Loss-Based Dual-Scale Dual-Softmax Transformer for Multisource Subjects Transfer Learning in Motor Imagery Recognition

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 14
• Main Authors: Luo, Jing, Li, Jundong, Mao, Qi, Liu, Yu, Yan, Wenyao, Xue, Yanmin, Shi, Zhenghao, Hei, Xinhong
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Brain modeling; Brain–computer interfaces (BCIs); Convolution; electroencephalogram (EEG) recognition; Electroencephalography; Feature extraction; Location awareness; mirror center loss; Mirrors; motor imagery (MI); Motors; Neural activity; Transfer learning; transformer; Transformers

Accurate recognition of motor imagery (MI)-based electroencephalogram (EEG) signals is crucial for the performance of brain-computer interface (BCI). Given the limited number of EEG signals from a target subject, localizing neural activity in the sensorimotor cortex of the brain and transferring kno...