Abstract
Recently, in order to overcome the disadvantages of unimodal brain-imaging modalities such as low signal-to-noise ratio and vulnerability to motion artifact and to improve system performance, a multimodal imaging system (so-called hybrid system) has been emerging as an attractive alternative. In the present study, to meet the increasing demand on a hybrid brain-imaging data, we introduce open access datasets of electroencephalography (EEG) and near-infrared spectroscopy (NIRS) simultaneously measured during various cognitive tasks. The datasets contain BCI data such as motor imagery (MI)-, and mental arithmetic (MA), and word generation (WG)-related brain signals, and cognitive task data such as n-back (NB)-, and discrimination/selection response (DSR)-related brain signals. We provide the reference results of these datasets, which were validated using analysis pipelines widely used in related research fields. In particular, it was confirmed from classification analysis that a hybrid EEG-NIRS system can yield better classification accuracy than each of unimodal brain-imaging systems.
| Original language | English |
|---|---|
| Title of host publication | 2018 6th International Conference on Brain-Computer Interface, BCI 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-4 |
| Number of pages | 4 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538625743 |
| DOIs | |
| Publication status | Published - 2018 Mar 9 |
| Event | 6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of Duration: 2018 Jan 15 → 2018 Jan 17 |
Other
| Other | 6th International Conference on Brain-Computer Interface, BCI 2018 |
|---|---|
| Country | Korea, Republic of |
| City | GangWon |
| Period | 18/1/15 → 18/1/17 |
Fingerprint
Keywords
- Brain-computer interface
- EEG
- Hybrid BCI
- NIRS
- Open access dataset
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Behavioral Neuroscience
Cite this
Open access repository for hybrid EEG-NIRS data. / Shin, Jaeyoung; Von Luhmann, Alexander; Blankertz, Benjamin; Kim, Do Won; Mehnert, Jan; Jeong, Jichai; Hwang, Han Jeong; Muller, Klaus.
2018 6th International Conference on Brain-Computer Interface, BCI 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Open access repository for hybrid EEG-NIRS data
AU - Shin, Jaeyoung
AU - Von Luhmann, Alexander
AU - Blankertz, Benjamin
AU - Kim, Do Won
AU - Mehnert, Jan
AU - Jeong, Jichai
AU - Hwang, Han Jeong
AU - Muller, Klaus
PY - 2018/3/9
Y1 - 2018/3/9
N2 - Recently, in order to overcome the disadvantages of unimodal brain-imaging modalities such as low signal-to-noise ratio and vulnerability to motion artifact and to improve system performance, a multimodal imaging system (so-called hybrid system) has been emerging as an attractive alternative. In the present study, to meet the increasing demand on a hybrid brain-imaging data, we introduce open access datasets of electroencephalography (EEG) and near-infrared spectroscopy (NIRS) simultaneously measured during various cognitive tasks. The datasets contain BCI data such as motor imagery (MI)-, and mental arithmetic (MA), and word generation (WG)-related brain signals, and cognitive task data such as n-back (NB)-, and discrimination/selection response (DSR)-related brain signals. We provide the reference results of these datasets, which were validated using analysis pipelines widely used in related research fields. In particular, it was confirmed from classification analysis that a hybrid EEG-NIRS system can yield better classification accuracy than each of unimodal brain-imaging systems.
AB - Recently, in order to overcome the disadvantages of unimodal brain-imaging modalities such as low signal-to-noise ratio and vulnerability to motion artifact and to improve system performance, a multimodal imaging system (so-called hybrid system) has been emerging as an attractive alternative. In the present study, to meet the increasing demand on a hybrid brain-imaging data, we introduce open access datasets of electroencephalography (EEG) and near-infrared spectroscopy (NIRS) simultaneously measured during various cognitive tasks. The datasets contain BCI data such as motor imagery (MI)-, and mental arithmetic (MA), and word generation (WG)-related brain signals, and cognitive task data such as n-back (NB)-, and discrimination/selection response (DSR)-related brain signals. We provide the reference results of these datasets, which were validated using analysis pipelines widely used in related research fields. In particular, it was confirmed from classification analysis that a hybrid EEG-NIRS system can yield better classification accuracy than each of unimodal brain-imaging systems.
KW - Brain-computer interface
KW - EEG
KW - Hybrid BCI
KW - NIRS
KW - Open access dataset
UR - http://www.scopus.com/inward/record.url?scp=85050796873&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050796873&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2018.8311523
DO - 10.1109/IWW-BCI.2018.8311523
M3 - Conference contribution
AN - SCOPUS:85050796873
VL - 2018-January
SP - 1
EP - 4
BT - 2018 6th International Conference on Brain-Computer Interface, BCI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -