  A major issue in Motor Imagery Brain-Computer Interfaces (MI-BCIs) is their
poor classification accuracy and the large amount of data that is required for
subject-specific calibration. This makes BCIs less accessible to general users
in out-of-the-lab applications. This study employed deep transfer learning for
development of calibration-free subject-independent MI-BCI classifiers. Unlike
earlier works that applied signal preprocessing and feature engineering steps
in transfer learning, this study adopted an end-to-end deep learning approach
on raw EEG signals. Three deep learning models (MIN2Net, EEGNet and
DeepConvNet) were trained and compared using an openly available dataset. The
dataset contained EEG signals from 55 subjects who conducted a left- vs.
right-hand motor imagery task. To evaluate the performance of each model, a
leave-one-subject-out cross validation was used. The results of the models
differed significantly. MIN2Net was not able to differentiate right- vs.
left-hand motor imagery of new users, with a median accuracy of 51.7%. The
other two models performed better, with median accuracies of 62.5% for EEGNet
and 59.2% for DeepConvNet. These accuracies do not reach the required threshold
of 70% needed for significant control, however, they are similar to the
accuracies of these models when tested on other datasets without transfer
learning.
