Ternary Near-Infrared Spectroscopy Brain-Computer Interface With Increased Information Transfer Rate Using Prefrontal Hemodynamic Changes During Mental Arithmetic, Breath-Holding, and Idle State

The implementation of a multi-class brain-computer interface (BCI) is an efficient way to increase the information transfer rate (ITR) generally expressed in a unit of bits/trial. The improvement of ITR is of specific importance for near-infrared spectroscopy (NIRS)-BCI, because brain hemodynamic responses recorded by NIRS are much slower than the electrophysiological responses of the brain. In this paper, to implement a ternary NIRS-BCI with increased ITR, we used prefrontal cortex (PFC) hemodynamic changes induced by breath-holding (i.e., the voluntary suppression of breathing movements), which have never been used in the field of BCIs. Additionally, we used traditional BCI tasks such as mental arithmetic and idle state to implement ternary NIRS-BCI. As a result, an average offline ternary classification accuracy of 72.6 +/- 10.7% could be achieved, which is the best performance of NIRS-BCI based on PFC hemodynamic changes reported to date. Because the number of available input commands was increased and the classification accuracy remained at an acceptable level, the ITR of the ternary BCI (0.51 +/- 0.29 bits/trial) was 1.6 times higher than that of the traditional binary BCI (0.31 +/- 0.21 bits/trial). Although the hemodynamic changes induced by the breath-holding were not caused by mental state changes, breath-holding proved to be a promising hybrid BCI task for implementing more efficient ternary NIRS-BCI.This work was supported in part by the ICT Research and Development Program of MSIT/IITP, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought through AR/VR interface, under Grant 2017-0-00432, and in part by the Brain Research Program through the National Research Foundation of Korea, Ministry of Science and ICT, under Grant NRF-2015M3C7A1031969 and Grant NRF-2017R1A6A3A01003543