Online EEG Classification of Covert Speech for Brain-Computer Interfacing

Rezazadeh Sereshkeh, Alborz 1, 2 ; Trott, Robert 1, 3 ; Bricout, Aurelien 1, 4 ; Chau, Tom 1, 2

1. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital; 2. Institute of Biomaterials and Biomedical Engineering, University of Toronto; 3. School of Computer Science, Engineering & MathematicsFaculty of Science & Engineering, Flinders University; 4. Polytech Grenoble, Department of Health Information TechnologyUniversité Joseph Fourier

Brain-computer interfaces (BCIs) for communication can be non-intuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10 seconds of mental repetitions of the word “no” and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10 seconds each of covert repetition of the words “yes” and “no”. Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of 75.9%±11.4 was reached across participants in the online classification of no vs. rest, with 10 out of 12 participants surpassing the chance level (60.0% for p<0.05). The online classification of yes vs. no yielded an average accuracy of 69.3%±14.1, with 8 participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.