Cross-Subject Cognitive Workload Recognition Based on EEG and Deep Domain Adaptation
Yueying Zhou(Nanjing University of Aeronautics and Astronautics), Daoqiang Zhang(Nanjing University of Aeronautics and Astronautics), Xia Wu(Beijing Institute of Technology), Xuyun Wen(Beijing Normal University), Peiliang Gong(Nanjing University of Aeronautics and Astronautics), Pengpai Wang(Nanjing University of Aeronautics and Astronautics), Fulin Wei(Beijing Institute of Technology)
Cited by 32
Related Papers
Impairment and compensation coexist in amnestic MCI default mode network
|NeuroImage|2009|328
Altered default mode network connectivity in alzheimer's disease—A resting functional MRI and bayesian network study
|Human Brain Mapping|2011|208
Cognitive Workload Recognition Using EEG Signals and Machine Learning: A Review
|IEEE Transactions on Cognitive and Developmental Systems|2021|207
The nuclear transcription factor FoxG1 affects the sensitivity of mimetic aging hair cells to inflammation by regulating autophagy pathways
|Redox Biology|2019|169
A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals
|Neurocomputing|2020|151