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

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Citation

Lee, M.-H., Fazli, S., Mehnert, J., & Lee, S.-W. (2015). Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI. Pattern Recognition, 48(8), 2725-2737. doi:10.1016/j.patcog.2015.03.010.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0026-A3F0-B
Abstract
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.