TY - JOUR
T1 - Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI
AU - Lee, Minho
AU - Fazli, Siamac
AU - Mehnert, Jan
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2012-005741 ).
Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - 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.
AB - 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.
KW - Classifier combination
KW - Combined EEG-NIRS
KW - Hybrid brain-computer interfacing
KW - Subject-dependent classification
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U2 - 10.1016/j.patcog.2015.03.010
DO - 10.1016/j.patcog.2015.03.010
M3 - Article
AN - SCOPUS:84928298407
SN - 0031-3203
VL - 48
SP - 2725
EP - 2737
JO - Pattern Recognition
JF - Pattern Recognition
IS - 8
M1 - 5378
ER -