EEGSym: Overcoming Inter-subject Variability in Motor Imagery Based BCIs with Deep Learning

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; p. 1
• Main Authors: Perez-Velasco, Sergio, Santamaria-Vazquez, Eduardo, Martinez-Cagigal, Victor, Marcos-Martinez, Diego, Hornero, Roberto
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Brain Computer Interface (BCI); Brain modeling; Classification; Computer architecture; Convolutional neural networks; Datasets; Decoding; Deep learning; Deep Learning (DL); EEG; Electrodes; Electroencephalography; Feature extraction; Human-computer interface; Imagery; Inter-subject; Interfaces; Machine learning; Mental task performance; Model accuracy; Motor Imagery; Motor skill learning; Neural networks; Transfer learning

In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym . Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variability and reducing BCI ineffi...