TY  - JOUR 
AU  - Perez-Velasco, Sergio
AU  - Santamaria-Vazquez, Eduardo
AU  - Martinez-Cagigal, Victor
AU  - Marcos-Martinez, Diego
AU  - Hornero, Roberto
PY  - 2022
SN  - 1534-4320
UR  - https://uvadoc.uva.es/handle/10324/81636
AB  - 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...
LA  - eng
PB  - IEEE
KW  - Brain computer interface (BCI), deep learning (DL), motor imagery, transfer learning, inter-subject
TI  - EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning
DO  - 10.1109/TNSRE.2022.3186442
ER  -