Multi-subject classification of Motor Imagery EEG signals using transfer learning in neural networks

Brain-Computer Interfaces are new technologies with a fast development due to their possible usages, which still require overcoming some challenges to be readily usable. The paradigm of motor imagery is among the ones in these types of systems where the pipeline is tuned to work with only one person as it fails to classify the signals of a different person. Deep Learning methods have been gaining attention for tasks involving high-dimensional unstructured data, like EEG signals, but fail to generalize when trained on small datasets. In this work, to acquire a benchmark, we evaluate the performance of several classifiers while decoding signals from a new subject using a leave-one-out approach. Then we test the classifiers on the previous experiment and a method based on transfer learning in neural networks to classify the signals of multiple persons at a time. The resulting neural network classifier achieves a classification accuracy of 73% on the evaluation sessions of four subjects at a time and 74% on three at a time on the BCI competition IV 2a dataset.