Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI. Issue 8 (August 2015)

• Record Type: Journal Article
• Authors: Lee, Min-Ho; Fazli, Siamac; Mehnert, Jan; Lee, Seong-Whan
• Is Part Of: Pattern recognition. Volume 48:Issue 8(2015:Aug.)
• Page Start: 2725
• Page End: 2737
• Publication Date: 2015-08
• Subjects: Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.03.010
• Languages: English
• ISSNs: 0031-3203

Abstract

<abstract abstract-type="author" id="ab0005"> <title id="sect0005">Abstract</title> <sec> <p id="sp0060">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.</p> </sec> </abstract>