  The introduction of deep learning and transfer learning techniques in fields
such as computer vision allowed a leap forward in the accuracy of image
classification tasks. Currently there is only limited use of such techniques in
neuroscience. The challenge of using deep learning methods to successfully
train models in neuroscience, lies in the complexity of the information that is
processed, the availability of data and the cost of producing sufficient high
quality annotations. Inspired by its application in computer vision, we
introduce transfer learning on electrophysiological data to enable training a
model with limited amounts of data. Our method was tested on the dataset of the
BCI competition IV 2a and compared to the top results that were obtained using
traditional machine learning techniques. Using our DL model we outperform the
top result of the competition by 33%. We also explore transferability of
knowledge between trained models over different experiments, called
inter-experimental transfer learning. This reduces the amount of required data
even further and is especially useful when few subjects are available. This
method is able to outperform the standard deep learning methods used in the BCI
competition IV 2b approaches by 18%. In this project we propose a method that
can produce reliable electroencephalography (EEG) signal classification, based
on modest amounts of training data through the use of transfer learning.
