Improving the performance of brain-computer interface using multi-modal neuroimaging

Min Ho Lee, Siamac Fazli, Jan Mehnert, Seong Whan Lee

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Nevertheless, most current BCI systems rely on cues or tasks to which the subject has to react (i.e., synchronous BCIs). Such systems have limited applications in the real world. It is more desirable for the user to decide himself, when he likes to control a device. However, these so-called asynchronous BCI systems, that rely on electroencephalogram (EEG) measurements show the demand for higher accuracy and stability. Previously, hybrid BCI systems, relying on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, have been shown to increase the classification performance of (synchronous) motor imagery (MI) tasks. Here we present the first asynchronous hybrid BCI with encouraging results.

Original languageEnglish
Pages511-515
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 - Naha, Okinawa, Japan
Duration: 2013 Nov 52013 Nov 8

Other

Other2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
CountryJapan
CityNaha, Okinawa
Period13/11/513/11/8

Keywords

  • Asynchronous BCI
  • Combined EEG-NIRS
  • Hybrid Brain-Computer Interfacing
  • Multi-class Classification

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Lee, M. H., Fazli, S., Mehnert, J., & Lee, S. W. (2013). Improving the performance of brain-computer interface using multi-modal neuroimaging. 511-515. Paper presented at 2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013, Naha, Okinawa, Japan. https://doi.org/10.1109/ACPR.2013.132