کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10361298 870090 2015 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 48, Issue 8, August 2015, Pages 2725-2737
نویسندگان
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