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

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.

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 Urheber:
Lee, Min-Ho1, Autor
Fazli, Siamac1, Autor
Mehnert, Jan2, Autor           
Lee, Seong-Whan1, Autor
Affiliations:
1Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea, ou_persistent22              
2TU Berlin, Germany, ou_persistent22              

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Schlagwörter: Hybrid brain–computer interfacing; Combined EEG–NIRS; Classifier combination; Subject-dependent classification
 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2014-12-302014-05-232015-03-082015-03-182015-08
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.patcog.2015.03.010
 Art des Abschluß: -

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Titel: Pattern Recognition
  Andere : Pattern Recognit.
Genre der Quelle: Zeitschrift
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Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 48 (8) Artikelnummer: - Start- / Endseite: 2725 - 2737 Identifikator: ISSN: 0031-3203
CoNE: https://pure.mpg.de/cone/journals/resource/954925431363