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Open Access Dataset for EEG plus NIRS Single-Trial Classification

Authors
Shin, Jaeyoungvon Luehmann, AlexanderBlankertz, BenjaminKim, Do-WonJeong, JichaiHwang, Han-JeongMueller, Klaus-Robert
Issue Date
10월-2017
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Brain-computer interface (BCI); electroencephalography (EEG); hybrid BCI; mental arithmetic; motor imagery; near-infrared spectroscopy (NIRS); open access dataset
Citation
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.25, no.10, pp.1735 - 1745
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume
25
Number
10
Start Page
1735
End Page
1745
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/82149
DOI
10.1109/TNSRE.2016.2628057
ISSN
1534-4320
Abstract
We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left versus right hand motor imagery; mental arithmetic versus resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be enhanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.
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Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
Graduate School > Department of Electronics and Information Engineering > 1. Journal Articles
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