Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems

We investigate time domain parameters called high order moments in functional Near Infrared Spectroscopy (fNIRS) signal and propose to use them as new brain features in fNIRS-based Brain Computer Interface (BCI) research. These high order moments are well appropriate with fNIRS data without any special preprocessing or filtering step. Therefore, they could be used to guide users in feedback fNIRS-based BCI experiments. We performed experiments on motor imagery and person identification problems with the 2nd order moment, 4th order moment and a combination of these moments. Experimental results showed that these features provided high accuracy. For motor imagery problem, our system could achieve accuracy up to 99.5% for subject independent problem and varies between 86.5±5.4% and 97.0±2.1% for subject dependent problem. For person identification problem, our system could achieve accuracy nearly 100%. Comparing with other systems that used non-filtered raw signal as feature, these features are more stable than the raw signal because of noise reduction. We also found that the 2nd order moment alone could be an excellent and efficient feature for fNIRS-based BCI systems.