Improving Silent Speech BCI Training Procedures Through Transfer from Overt to Silent Speech

Abstract

Silent speech Brain-Computer Interfaces (BCIs) try to decode imagined speech from brain activity. Those BCIs require a tremendous amount of training data usually collected during mentally and physically exhausting sessions in which participants silently repeat words presented on a screen for several hours. Within this work we present an approach to overcome those exhausting sessions by training a silent speech classifier on data recorded while speaking certain words and transferring this classifier to EEG data recorded during silent repetition of the same words. This approach does not only allow for a less mentally and physically exhausting training procedure but also for a more productive one as the overt speech output can be used for interaction while the classifier for silent speech is trained simultaneously. We evaluated our approach in a study in which 15 participants navigated a virtual robot on a screen in a game like scenario through a maze once with 5 overtly spoken and once with the same 5 silently spoken command words. In an offline analysis we trained a classifier on overt speech data and let it predict silent speech data. Our classification results do not only show successful results for the transfer (61.78%) significantly above chance level but also comparable results to a standard silents speech classifier (71.48%) trained and tested on the same data. These results illustrate the potential of the method to replace the currently tedious training procedures for silent speech BCIs with a more comfortable, engaging and productive approach by a transfer from overt to silent speech.