Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI

Abstract

Brain–computer interfaces (BCIs) allow users to control external devices by their intentions. Currently, most BCI systems are synchronous. They rely on cues or tasks to which a subject has to react. In order to design an asynchronous BCI one needs to be able to robustly detect an idle class. In this study, we examine whether multi-modal neuroimaging, based on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, can assist in the robust detection of the idle class within a sensory motor rhythm-based BCI paradigm. We propose two types of subject-dependent classification strategies to combine the information of both modalities. Our results demonstrate that not only idle-state decoding can be significantly improved by exploiting the complementary information of multi-modal recordings, but also it is possible to minimize the delay of the system, caused by the slow inherent hemodynamic response of the NIRS signal.