Open Access Dataset for EEG+NIRS Single-Trial Classification

Jaeyoung Shin, Alexander Von Luhmann, Benjamin Blankertz, Do Won Kim, Jichai Jeong, Han Jeong Hwang, Klaus Muller

Research output: Contribution to journalArticle

23 Citations (Scopus)

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.

Original languageEnglish
Article number7742400
Pages (from-to)1735-1745
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume25
Issue number10
DOIs
Publication statusPublished - 2017 Oct 1

Fingerprint

Brain-Computer Interfaces
Brain computer interface
Near infrared spectroscopy
Near-Infrared Spectroscopy
Electroencephalography
Hybrid Computers
Imagery (Psychotherapy)
Signal analysis
Artifacts
Hand
Datasets
Research
Experiments

Keywords

  • Brain-computer interface (BCI)
  • electroen-cephalography (EEG)
  • hybrid BCI
  • mental arithmetic
  • motor imagery
  • near-infrared spectroscopy (NIRS)
  • open access dataset

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

Shin, J., Von Luhmann, A., Blankertz, B., Kim, D. W., Jeong, J., Hwang, H. J., & Muller, K. (2017). Open Access Dataset for EEG+NIRS Single-Trial Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1735-1745. [7742400]. https://doi.org/10.1109/TNSRE.2016.2628057

Open Access Dataset for EEG+NIRS Single-Trial Classification. / Shin, Jaeyoung; Von Luhmann, Alexander; Blankertz, Benjamin; Kim, Do Won; Jeong, Jichai; Hwang, Han Jeong; Muller, Klaus.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 25, No. 10, 7742400, 01.10.2017, p. 1735-1745.

Research output: Contribution to journalArticle

Shin, J, Von Luhmann, A, Blankertz, B, Kim, DW, Jeong, J, Hwang, HJ & Muller, K 2017, 'Open Access Dataset for EEG+NIRS Single-Trial Classification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, 7742400, pp. 1735-1745. https://doi.org/10.1109/TNSRE.2016.2628057
Shin, Jaeyoung ; Von Luhmann, Alexander ; Blankertz, Benjamin ; Kim, Do Won ; Jeong, Jichai ; Hwang, Han Jeong ; Muller, Klaus. / Open Access Dataset for EEG+NIRS Single-Trial Classification. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017 ; Vol. 25, No. 10. pp. 1735-1745.
@article{e81fad8f2a9f4c57ae7abb115e3c3650,
title = "Open Access Dataset for EEG+NIRS Single-Trial Classification",
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.",
keywords = "Brain-computer interface (BCI), electroen-cephalography (EEG), hybrid BCI, mental arithmetic, motor imagery, near-infrared spectroscopy (NIRS), open access dataset",
author = "Jaeyoung Shin and {Von Luhmann}, Alexander and Benjamin Blankertz and Kim, {Do Won} and Jichai Jeong and Hwang, {Han Jeong} and Klaus Muller",
year = "2017",
month = "10",
day = "1",
doi = "10.1109/TNSRE.2016.2628057",
language = "English",
volume = "25",
pages = "1735--1745",
journal = "IEEE Transactions on Neural Systems and Rehabilitation Engineering",
issn = "1534-4320",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "10",

}

TY - JOUR

T1 - Open Access Dataset for EEG+NIRS Single-Trial Classification

AU - Shin, Jaeyoung

AU - Von Luhmann, Alexander

AU - Blankertz, Benjamin

AU - Kim, Do Won

AU - Jeong, Jichai

AU - Hwang, Han Jeong

AU - Muller, Klaus

PY - 2017/10/1

Y1 - 2017/10/1

N2 - 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.

AB - 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.

KW - Brain-computer interface (BCI)

KW - electroen-cephalography (EEG)

KW - hybrid BCI

KW - mental arithmetic

KW - motor imagery

KW - near-infrared spectroscopy (NIRS)

KW - open access dataset

UR - http://www.scopus.com/inward/record.url?scp=85032892603&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032892603&partnerID=8YFLogxK

U2 - 10.1109/TNSRE.2016.2628057

DO - 10.1109/TNSRE.2016.2628057

M3 - Article

VL - 25

SP - 1735

EP - 1745

JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering

JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering

SN - 1534-4320

IS - 10

M1 - 7742400

ER -