The majority of proposed NIRS-BCIs has considered binary classification. Studies considering high-order classification problems have yielded average accuracies that are less than favorable for practical communication. Consequently, there is a paucity of evidence supporting online classification of more than two mental states using NIRS. We developed an online ternary NIRS-BCI that supports the verbal fluency task (VFT), Stroop task and rest. The system utilized two sessions dedicated solely to classifier training. Additionally, samples were collected prior to each period of online classification to update the classifier. Using a continuous-wave spectrometer, measurements were collected from the prefrontal and parietal cortices while 11 able-bodied adult participants were cued to perform one of the two cognitive tasks or rests. Each task was used to indicate the desire to select a particular letter on a scanning interface, while rest avoided selection. Classification was performed using 25 iteration of bagging with a linear discriminant base classifier. Classifiers were trained on 10-dimensional feature sets. The BCI’s classification decision was provided as feedback. An average online classification accuracy of 74.2±14.8% was achieved, representing an ITR of 1.31±0.86 bits/min. The results demonstrate that online communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest. Our findings encourage continued efforts to enhance the ITR of NIRS-BCIs.
