Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 2; pp. 535 - 545
• Main Authors: Zhao, He, Zheng, Qingqing, Ma, Kai, Li, Huiqi, Zheng, Yefeng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptation models; Algorithms; Brain Mapping; Brain modeling; Brain-Computer Interfaces; Calibration; Classification; Classifiers; Cross subject; Data mining; Data models; Datasets; Deep Learning; deep neural network (DNN); domain adaptation; Domains; EEG; Electroencephalography; electroencephalography (EEG); Electroencephalography - classification; Feature extraction; Human-computer interface; Humans; Image classification; Image Processing, Computer-Assisted; Mental task performance; motor imagery (MI); Movement; Neural Networks, Computer; Neuroimaging; Representations; Reproducibility of Results; Signal Processing, Computer-Assisted; Task analysis; Testing time

In the context of motor imagery, electroencephalography (EEG) data vary from subject to subject such that the performance of a classifier trained on data of multiple subjects from a specific domain typically degrades when applied to a different subject. While collecting enough samples from each subj...