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

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

doi:10.1109/ACPR.2013.132

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

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

Department of Brain and Cognitive Engineering

Research output: Contribution to conference › Paper › peer-review

2 Citations (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 language	English
Pages	511-515
Number of pages	5
DOIs	https://doi.org/10.1109/ACPR.2013.132
Publication status	Published - 2013
Event	2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 - Naha, Okinawa, Japan Duration: 2013 Nov 5 → 2013 Nov 8

Other

Other	2013 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
Country/Territory	Japan
City	Naha, Okinawa
Period	13/11/5 → 13/11/8

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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10.1109/ACPR.2013.132

Cite this

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

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Keywords

ASJC Scopus subject areas

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