On The Deep Learning Models for EEG-based Brain-Computer Interface Using Motor Imagery

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; p. 1
• Main Authors: Zhu, Hao, Forenzo, Dylan, He, Bin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: BCI; Brain; Brain modeling; Brain-computer interface; Classification; Classifiers; Computer applications; Convolution; Convolutional neural networks; Datasets; Deep learning; EEG; Electroencephalography; Feature extraction; Human-computer interface; Imagery; Implants; Machine learning; Mental task performance; motor imagery; Task analysis

Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and resulted in multiple models. Finding the best perf...