Title: Online classification of imagined speech using functional near-infrared spectroscopy signals,
Abstract: Most brain-computer interfaces (BCIs) based on functional near-infrared
spectroscopy (fNIRS) require that users perform mental tasks such as motor
imagery, mental arithmetic, or music imagery to convey a message or to answer
simple yes or no questions. These cognitive tasks usually have no direct
association with the communicative intent, which makes them difficult for users
to perform. In this paper, a 3-class intuitive BCI is presented which enables
users to directly answer yes or no questions by covertly rehearsing the word
'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of
unconstrained rest which constitutes the third discernable task. Twelve
participants each completed one offline block and six online blocks over the
course of 2 sessions. The mean value of the change in oxygenated hemoglobin
concentration during a trial was calculated for each channel and used to train
a regularized linear discriminant analysis (RLDA) classifier. By the final
online block, 9 out of 12 participants were performing above chance (p<0.001),
with a 3-class accuracy of 83.8+9.4%. Even when considering all participants,
the average online 3-class accuracy over the last 3 blocks was 64.1+20.6%, with
only 3 participants scoring below chance (p<0.001). For most participants,
channels in the left temporal and temporoparietal cortex provided the most
discriminative information. To our knowledge, this is the first report of an
online fNIRS 3-class imagined speech BCI. Our findings suggest that imagined
speech can be used as a reliable activation task for selected users for the
development of more intuitive BCIs for communication.