Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces
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作者:
Hwang, Han-Jeong
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Hanyang Univ, Dept Biomed Engn, Seoul 133791, South Korea
Berlin Inst Technol, Machine Learning Grp, D-10587 Berlin, GermanyHanyang Univ, Dept Biomed Engn, Seoul 133791, South Korea
Hwang, Han-Jeong
[1
,2
]
Lim, Jeong-Hwan
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Hanyang Univ, Dept Biomed Engn, Seoul 133791, South KoreaHanyang Univ, Dept Biomed Engn, Seoul 133791, South Korea
Lim, Jeong-Hwan
[1
]
Kim, Do-Won
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Hanyang Univ, Dept Biomed Engn, Seoul 133791, South KoreaHanyang Univ, Dept Biomed Engn, Seoul 133791, South Korea
Kim, Do-Won
[1
]
Im, Chang-Hwan
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Hanyang Univ, Dept Biomed Engn, Seoul 133791, South KoreaHanyang Univ, Dept Biomed Engn, Seoul 133791, South Korea
Im, Chang-Hwan
[1
]
机构:
[1] Hanyang Univ, Dept Biomed Engn, Seoul 133791, South Korea
[2] Berlin Inst Technol, Machine Learning Grp, D-10587 Berlin, Germany
A number of recent studies have demonstrated that near-infrared spectroscopy (NIRS) is a promising neuroimaging modality for brain-computer interfaces (BCIs). So far, most NIRS-based BCI studies have focused on enhancing the accuracy of the classification of different mental tasks. In the present study, we evaluated the performances of a variety of mental task combinations in order to determine the mental task pairs that are best suited for customized NIRS-based BCIs. To this end, we recorded event-related hemodynamic responses while seven participants performed eight different mental tasks. Classification accuracies were then estimated for all possible pairs of the eight mental tasks (C-8(2) = 28). Based on this analysis, mental task combinations with relatively high classification accuracies frequently included the following three mental tasks: "mental multiplication," "mental rotation," and "right-hand motor imagery." Specifically, mental task combinations consisting of two of these three mental tasks showed the highest mean classification accuracies. It is expected that our results will be a useful reference to reduce the time needed for preliminary tests when discovering individual-specific mental task combinations. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)