A Transfer-Learning Approach for Cross-Subjects EEG Data Classification

EEG data classification is a difficult task, especially when it comes to classifying between different individuals or different sessions. In these cases, it is necessary to use a so-called Domain Adaptation technique, but these can be tricky to implement and interpret. We propose a methodology based on the extension of the EEGNet network, decoupling the learning of subject-independent and subject-dependent parameters and from the optimized use of the available database. The result is a robust and easy to implement methodology that we named TL-EEGNet network.