Handling Few Training Data: Classifier Transfer Between Different Types of Error-Related Potentials.

This paper proposes an application oriented approach that enables to transfer a classifier trained within an experimental scenario into a more complex application scenario or a specific rehabilitation situation which do not allow to collect sufficient training data within a reasonable amount of time. The proposed transfer approach is not limited to be applied to the same type of event-related potential. We show that a classifier trained to detect a certain brain pattern can be used successfully to detect another brain pattern, which is expected to be similar to the first one. In particular a classifier is transferred between two different types of error-related potentials (ErrPs) within the same subject. The classifier trained on observation ErrPs is used to detect interaction ErrPs, since twice as much training data is collected for observation ErrPs compared to interaction ErrPs during the same calibration time. Our results show that the proposed transfer approach is feasible and outperforms another approach, in which a classifier is transferred between different subjects but the same type of ErrP is used to train and test the classifier. The proposed approach is a promising way to handle few training data and to reduce calibration time in ErrP-based brain-computer interfaces.