Classification of mental tasks in the prefrontal cortex using fNIRS

Functional near infrared spectroscopy (fNIRS) is rapidly gaining interest in both the Neuroscience, as well as the Brain-Computer-Interface (BCI) community. Despite these efforts, most single-trial analysis of fNIRS data is focused on motor-imagery, or mental arithmetics. In this study, we investigate the suitability of different mental tasks, namely mental arithmetics, word generation and mental rotation for fNIRS based BCIs. We provide the first systematic comparison of classification accuracies achieved in a sample study. Data was collected from 10 subjects performing these three tasks. An optode template with 8 channels was chosen which covers the prefrontal cortex and only requires less than 3 minutes for setup. Two-class accuracies of up to 71% average across all subjects for mental arithmetics, 70% for word generation and 62% for mental rotation were achieved discriminating these tasks from a relax state. We thus lay the foundation for fNIRS based BCI using additional mental strategies than motor imagery and mental arithmetics. The tasks were chosen in a way that they might be used for user state monitoring, as well.